ELIZA—a computer program for the study of natural language communication between man and machine
Open accessAbstract
Communication Between Man And Machine,JosEPh ~VEIZENBA UM Massach.usclls[nshl-ute qf Tcchnu[ogg,* Cambridge, Mass.ELIZA is a program operating within the MAC time-sharing system at MIT which makes certain kinds of natural language conversation between man and computer possible.Input sentences are analyzed on the basis of decomposition rules which are triggered by key words appearing in the input text.Responses are generated by reassembly rules associated with selected decomposition rules.The fundamental technical problems with which ELIZA is concerned are: (1) the identification of key words, (2) the discovery of minimal context, (3) the choice of appropriate transformations, (4) generation of responses in the absence of key words, and (5) the provision of an editing capability for ELIZA "scripts".A discussion of some psychological issues relevant to the ELIZA approach as well as of future developments concludes the paper.Work reported herein was supported (iu prot) by Project MAC, an _\-lIT research program sponsored by the Advm~ced Research Pr,,jects Aget,cy, l)~l)ar~met~t ~,1" l)ofense, under Office of Naval Research Contract Number Nora'-4102(01).
AI snapshot
Written from the paper’s full text.
TL;DR
ELIZA is an early natural-language conversation program that scans input for keywords and applies associated decomposition and reassembly rules to generate responses. The paper describes its architecture, script mechanisms, and psychological implications, while stressing that ELIZA simulates understanding rather than possessing it.
Key findings
- ELIZA selects the highest-ranked keyword found in an input text, then tries matching decomposition rules and applies an associated reassembly rule; if no keyword is found, it can use the reserved `NONE` category for content-free remarks or the `MEMORY` mechanism to reuse earlier transformed text.
- Scripts are data rather than part of the program, so ELIZA is not tied to one recognition pattern or language; the paper states that scripts existed in Welsh and German as well as English.
- Implementation details include a keyword dictionary using a `KEY` vector of 128 contiguous computer words, hashing keywords to a 7-bit integer; the largest dictionary attempted contained about 50 keywords, and no list in the `KEY` vector held more than two key list structures.
- The discussion reports that the main serious scripts made ELIZA respond roughly like Rogerian psychotherapists, and that some subjects were hard to convince ELIZA was not human, which the paper calls a striking form of Turing’s test.
Methods
- Design: ELIZA was implemented on MIT’s MAC time-sharing system and written in MAD-SLIP for the IBM 7091 as printed; users typed natural-language statements, and a double carriage return transferred control to ELIZA.
- Data or sample: the paper uses illustrative conversations and script fragments, especially a Rogerian-style script, rather than a formal user study or quantitative dataset.
- Main analysis: technical exposition of decomposition and reassembly rules, keyword ranking via a keystack, substitution rules, equivalence classes, `MEMORY` and `NONE`, and script editing through the MAC `ED` program, followed by discussion of psychological credibility and future directions.
Limitations
- Stated: ELIZA discards its inputs except for a few transformed through the `MEMORY` machinery, lacks the capacity to store selected parts of input and draw valid conclusions, and has often aimed to conceal its lack of understanding.
- Stated: the script contains specific hypotheses about the world despite claims that the psychiatric interview format avoids storing real-world information; the paper also says bounds on ELIZA’s extendability come from the program, not from any script.
- Evident: no controlled evaluation or quantitative outcome data are reported; the evidence is illustrative transcripts and script examples, and the provided text is OCR-noisy and incomplete in places, making some technical details uncertain.
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Cites
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Claims the paper makes
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- Findingnone given§ Abstract
ELIZA is a program operating within the MAC time-sharing system at MIT that makes certain kinds of natural language conversation between man and computer possible.
“ELIZA is a program operating within the MAC time-sharing system at MIT which makes certain kinds of natural language conversation between man and computer possible.”
Quote in the paperNot a substantial claim - Findingnone given§ Discussion
ELIZA in its use so far has had as one of its principal objectives the concealment of its lack of understanding.
“ELIZA in its use so far has had as one of its principal objectives the cot~cetdment of its lack of understanding.”
Quote in the paperSubstantial claim - Findingreasoning§ Discussion
ELIZA performs best when its human correspondent is initially instructed to talk to it as one would to a psychiatrist.
“ELIZA performs best when its human correspondent is ilfitially instructed to “taJk” to it, via the typewriter of course, just as one would to a psychiatrist.”
Quote in the paperSubstantial claim - Interpretationreasoning§ Discussion
There are bounds on the extendability of ELIZA's understanding power that are a function of the ELIZA program itself and not of any script it may be given.
“But.there are bounds on the extendability of ELIZA's “understanding” power, which arc a function of the ELIZA program itself and not a function of any script it may be given.”
Quote in the paperSubstantial claim - Interpretationreasoning§ Discussion
The psychiatric interview form of an ELIZA script eliminates the need to store explicit information about the real world.
“the t?sychbJ:ric interview form of an I';LIZA script has the advantage that it eliminates the need of storing e.~:pUcit inform:tth?n about the real world.”
Quote in the paperSubstantial claim
- Interpretationreasoning§ Discussion
A switch of objectives from concealment to revelation of misunderstanding is a precondition to making an ELIZA-like program the basis for an effective natural language man-machine communication system.
“A switch of objectives from the concealmeat to the revelation of misunderstanding is seen as a precondition to making an ELIZA-like program the basis for an effective natural language man machine communication system.”
Quote in the paperNot a substantial claim
- Classified as probably not a substantial claim. 35
- May combine two claims. 9
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone given§ W H A T E L S E COMES TO M I N D WtIEN YOU T H I N K OF Y()(-II F A T H E R Bullies. D O E S T H A T H A \ -E A N Y T t I I N G TO DO W I T H T H E F A C T T I t A T Y O U R B O Y F R I E N I ) M A D E YOU C O M E H E R EThe largest dictionary so far attempted contains about 50 keywords.
“The largest dictionary so far attempted contains about 50 keywords.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingreasoning§ W H A T E L S E COMES TO M I N D WtIEN YOU T H I N K OF Y()(-II F A T H E R Bullies. D O E S T H A T H A \ -E A N Y T t I I N G TO DO W I T H T H E F A C T T I t A T Y O U R B O Y F R I E N I ) M A D E YOU C O M E H E R EELIZA scripts exist (at this writing) in Welsh and German as well as in English.
“ELIZA scripts exist (at, this writing) in Welsh attd Gernmn as well as in English.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingreasoning§ W H A T E L S E COMES TO M I N D WtIEN YOU T H I N K OF Y()(-II F A T H E R Bullies. D O E S T H A T H A \ -E A N Y T t I I N G TO DO W I T H T H E F A C T T I t A T Y O U R B O Y F R I E N I ) M A D E YOU C O M E H E R EThe set of transformation rules can be guaranteed to terminate with a decomposition rule which must match.
“Of course, lhe set of transformation rules can be guaranteed to terminate with a decomposition rule which nmst match.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone given§ W H A T E L S E COMES TO M I N D WtIEN YOU T H I N K OF Y()(-II F A T H E R Bullies. D O E S T H A T H A \ -E A N Y T t I I N G TO DO W I T H T H E F A C T T I t A T Y O U R B O Y F R I E N I ) M A D E YOU C O M E H E R EThe decomposition rule (0 K 0) will match any text in which the word K appears while (0) will match any text whatever.
“The decomposition rule (o K o) will match any text in which tile word K appears while (0) will match any text whaiever.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingnone given§ AbstractInput sentences are analyzed on the basis of decomposition rules triggered by key words appearing in the input text.
“Input sentences are analyzed on the basis of decomposition rules which are triggered by key words appearing in the input text.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone given§ DiscussionThe transformation rules that cause the input "Everybody hates me" to be transformed to "Can you think of anyone in particular," and other such rules, are based on quite specific hypotheses about the world.
“For e>:ample, the tran>formation rules which cause ihc input Everybody hates me to be transformed to Can you think of anyone in particular and other such are based on quite specific hypotheses about the world.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone given§ Consider again the input textThe current version of ELIZA requires that one keyword be associated with MEMORY and that exactly four transformations accompany that word in that context.
“The eurrent version of E L I Z A requires that one keyword be associated with M E M O R Y and that exactly four transformations aceoinpany that word in that context.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone given§ Consider again the input textWhenever 'MY' is the highest ranking keyword of a text, one of the transformations on the MEMORY list is randomly selected, and a copy of the text is transformed accordingly.
“Whenever it is the highest ranking keyword of a text one of the transformations on the M E M O R Y list is randomly selected, and a copy of the text is transformed accordingly.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Findingreasoning§ Consider again the input textThe system selects a decomposition rule for the highest ranking keyword found in an input text, attempts to match that text according to that decomposition rule and, failing to make a match, selects the next reassembly rule associated with the matching decomposition rule and applies it to generate an output text.
“The system described so far is ess(qltially one which selects ~t decomposition rule for the highest ranking keyword found in an input text, attempts to match that text according to that decomposition rule and, failing to make a match, selects the next reassembly rule associated with the matching decomposition rule and applies it.to generate an output texi.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingreasoning§ Consider again the input textThe mechanism makes it possible to test on key phrases as opposed to single key words.
“This mechanism makes it possible to, in effect, test on key phrases as opposed to single key words.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone given§ W H A T E L S E COMES TO M I N D WtIEN YOU T H I N K OF Y()(-II F A T H E R Bullies. D O E S T H A T H A \ -E A N Y T t I I N G TO DO W I T H T H E F A C T T I t A T Y O U R B O Y F R I E N I ) M A D E YOU C O M E H E R ENo list named in any of the words of the KEY vector contains more than two key list structures.
“No list named in any of the words of the KEY vector contains more than two key list structures.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingcitation§ DiscussionThe given language for ELIZA is English, and the other language, expressions of which are generated, is also English.
“The “given language” is English as is the “other language”, expressions of which arc generated.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingreasoning§ Consider again the input textWhen a text without keywords is encountered later and a certain counting mechanism is in a particular state and the stack in question is not empty, then the transformed text is printed out as the reply.
“When a text without keywords is encountered later and a certain counting mechanism is in a particular state and the stack in question is not empty, then the transformed text is printed out as the reply•”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Findingnone given§ DiscussionThe script that has formed the basis for most of this discussion has an overwhelmingly psychological orientation.
“The script that has formed the basis for most of this discussion happens to be one with an overwhehningly psychological orientation.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingreasoning§ DiscussionThe idea that the present ELIZA script contains no information about the real world is not entirely true.
“~.~: idea ihat the presettt ELIZA s{:rk)t contains no infocmation about tl~e real world i:s ,tot.e~tirely true.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingnone given§ W H A T E L S E COMES TO M I N D WtIEN YOU T H I N K OF Y()(-II F A T H E R Bullies. D O E S T H A T H A \ -E A N Y T t I I N G TO DO W I T H T H E F A C T T I t A T Y O U R B O Y F R I E N I ) M A D E YOU C O M E H E R EAn important property of ELIZA is that a script is data; i.e., it is not part of the program itself.
“An important property of ELIZA is that a script is data; i.e., it is not part of the program itself.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone given§ DiscussionSome subjects have been very hard to convince that ELIZA, with its present script, is not human.
“Seine subjects'have been very hm'd to convince t h a t E L I Z A (with its present script) is not hunmn.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingreasoning§ Consider again the input textChanges, additions and deletions of an ELIZA script may be made with considerable efficiency and on the basis of entirely contextual cues, without line numbers or other artificial devices.
“Suftice it to say that changes, additions and deletions of the script may be made with considerable efficiency and on the basis of entirely contextual cues, i.e., without resort to line numbers or any other artificial devices.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone given§ Consider again the input textThere is another mechanism which causes the system to respond more spectacularly in the absence of a key.
“There is, however, another mechanism which causes the s y s t e m to respond more spectacularly in the absence of a key.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingnone given§ AbstractResponses are generated by reassembly rules associated with selected decomposition rules.
“Responses are generated by reassembly rules associated with selected decomposition rules.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingreasoning§ Consider again the input textThe mechanism ensures that the complete set of reassembly rules associated with a given decomposition rule is cycled through before any repetitions occur.
“Tl, Js mechanism h~st,res that the complete set of reassernbly rules ~ssoeiated with a given deeomposh:ion rule is cycled through before any repetitions 0CCUY,”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone given§ Consider again the input textThe input text 'Am I sick' would elicit either 'Do you believe you are sick' or 'Why do you ask' depending on how many times the general form had already occurred.
“Then, the input text Am I siek would elicit either ])o you believe you are siek or Why do yotl ask i depending on how many tinles the general form had already occurred.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone given§ DiscussionAt this writing, the only serious ELIZA scripts that exist are ones that cause ELIZA to respond roughly as certain psychotherapists (Rogerians) would.
“At this writing, the only serious ELIZA scripts whieh exist are seine which cause E L I Z A to respond roughly as would certain psychotherapists (Rogerians).”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationreasoning§ W H A T E L S E COMES TO M I N D WtIEN YOU T H I N K OF Y()(-II F A T H E R Bullies. D O E S T H A T H A \ -E A N Y T t I I N G TO DO W I T H T H E F A C T T I t A T Y O U R B O Y F R I E N I ) M A D E YOU C O M E H E R EELIZA is not restricted to a particular set of recognition patterns or responses, nor even to any specific language.
“Hence, E L I Z A is not restricted to a particular set of recognition patterns or responses, indeed not even to any specific language.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Interpretationnone given§ DiscussionA large part of whatever elegance may be credited to ELIZA lies in the fact that ELIZA maintains the illusion of understanding with so little machinery.
“A large part of whatever elegance may be credited to ELIZA lies in the fact that ELIZA maintains the illusion of understanding with so little machinery.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationreasoning§ DiscussionSeen in the coldest possible light, ELIZA is a translating processor in Gorn's sense, but one especially constructed to work well with natural language text.
“Seen in the coldest possible light, ELIZA is a translating processor in Gorn's sense; however, it is one which has been especially constructed to work well with natural language text.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Interpretationreasoning§ DiscussionGorn's words fit ELIZA well.
“It is striking to note how well his words/fit ELIZA.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Interpretationcitation§ DiscussionIn principle, the given language could be the English of algebra word problems and the other language a machine code allowing a particular computer to solve the stated problems.
“In principle, the given hmguage could as well be the kind of English in which “word problems” in algebr~L am given to high school students and the other language, a machine code allowing a particular computer to “solve” the stated problems.(See Bobrow's program S T U D E N T [3].)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3] 3. Bom¢ow, D.G. Natural language input for a computer prob- lem solving system. Doctoral thesis, Math. Dept., MIT. Cambridge, Mass., 1964.
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationreasoning§ DiscussionFor a computer program to draw valid conclusions from what it is told, it must at least have the capacity to store selected parts of its inputs.
“In order for a computer program to be able to do that, it must at least have the capacity to store 8elected parts of its inputs.”
Quote in the paperNot a substantial claim- Rests on an assumption: The crucial test of understanding is not the ability to continue a conversation but to draw valid conclusions from what one is being told.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Interpretationreasoning§ DiscussionThe whole ELIZA script constitutes, in a loose way, a model of certain aspects of the world.
“The whole script constitutes, in a loose way, a model of certain aspects of the world.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
Interpretationreasoning§ DiscussionThe problem of drawing valid conclusions is more than one of storage; a great part of it is subsumed under the word "selected."
“Of course, the problem is more than one of storage.A great part of it is, in fact, subsumed under the word “selected” used just, above.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ DiscussionA translating processor, even if it operates only syntactically, generates corresponding expressions of another language to which meanings can be attributed.
“Given a language which already possesses semantic content, then a translating processor, even if it operates only syntactically, generates correspondittg expressions of another language to which we can attritmte as “meanings” (possibly multiple--the translator may not be one to one) the “semantic intents” of the generating source expressions; whether we find the result consistent or useful.or both”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [2] 2. Goax, S. Semiotic relationships in ambiguously stratified bmguage systems. Paper presented at Int. Colloq. Algebraic Linguistics and Automatic Theory, Hebrew U. of Jerusalem, Aug. 1964.
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationcitation§ DiscussionThe ELIZA program itself is merely a translating processor in the technical programming sense.
“It is useful to remember that the ELIZA program itself is merely a translating processor in the technical programming sense.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
Interpretationreasoning§ DiscussionELIZA shows how easy it is to create and maintain the illusion of understanding, and perhaps of judgment.
“E L I Z A shows, if nothing else, how easy it is to create and nmiutain the illusion of understanding, hence perhaps of j u d g m e n t”
Quote in the paperNot a substantial claim- Rests on an assumption: The human speaker contributes much to clothe ELIZA's responses in plausibility.
Classified as probably not a substantial claim.
Interpretationreasoning§ DiscussionTo encourage its conversational partner to offer inputs from which it can select remedial information, ELIZA must reveal its misunderstanding.
“But to encourage its conversational partner to offer inputs from which it can select remedial information, it, must ret, eal its mistmderstanding.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationreasoning§ Consider again the input textA given ELIZA script need not start out as a large, full-blown scenario.
“An important consequence of the editing facility built into ELIZA is that a given ELIZA script need not start out to be a large, full-blown scenario.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
The MAC system parameters generally contribute so little to the overall response time that conversational interaction with the computer need never involve truly intolerable delays.
“These latter parameters generally contribute so little to the overall response time that conversational interaction with the computer need never involve truly intolerable delays.”
The paper relies on this without support.
The determination that a word is not in a dictionary may well require more computation (i.e., time) than the location of a word which is represented.
“The iirst is serious in that the determination that a word is not in a dictionary may well require more computation (i.e., time) than the location of a word which is represented.”
The paper relies on this without support.
Almost none of the words in any given sentence are represented in the keyword dictionary.
“One stems from the fact that almost none of the words in any given sentence are repre.sented in the keyword dictionary.”
The paper relies on this without support.
The transformation rule mechanism of SLIP permits tagging of words in a text and their subsequent recovery on the basis of one of their tags.
“The transformation rule mechanism of SLIP is such that.it permits tagging of words in a text and their subsequent recovery on the basis of one of their tags.”
The paper relies on this without support.
A human speaker will not defend his illusion that he is being understood against all odds.
“But he will not defend his illusion (that he is being understood) against all odds.”
The paper relies on this without support.
In human conversation a speaker makes certain, perhaps generous, assumptions about his conversational partner.
“In tmnutn eonversat:on a speaker will make certain (perhaps generous) assmnl)lions about his conversational partner.”
The paper relies on this without support.
As long as the partner's responses can be interpreted consistently with those assumptions, the speaker's image of his partner remains unchanged and undamaged.
“As long as ~ it remains possible to interpret the latter's response~ consistently with those assumptions, the speaker's image of his partner remains unchanged, in particular, undanmged.”
The paper relies on this without support.
Responses difficult to interpret consistently may enhance the image of the partner through additional rationalizations.
“Responses wlfieh are difficult to so interpret m a y well result in an enhancement of the inmge of the partner, in additional rationalizations which then make more con> plicated interpretations of his responses reasonable.”
The paper relies on this without support.
When rationalizations become too massive and self-contradictory, the entire image may crumble and be replaced.
“When, however, such rationalizations become too inassive and even self-contradictory, the entire image may erumble and be replaeed by another (“He is not, after all, as s m a r t as I thought he was”).”
The paper relies on this without support.
When the conversational partner is a machine, credibility may be substituted for plausibility.
“When the conversational pa.rtner is a machine (the distinction between nmchine and prograin is here not useful) then the idea of credibility may weU be substituted for that. of pla.'uaibiStfl in the above.”
The paper relies on this without support.
The crucial test of understanding is not the ability to continue a conversation but to draw valid conclusions from what one is being told.
“The crucial test of understanding, as every teacher should know, is not the subject's ability to continue a conversation, but to draw valid conclusions h'om what. he is being told.”
The paper relies on this without support.
Important decisions increasingly tend to be made in response to computer output.
“I m p o r t a n t decisions increasingly tend to be made in response to computer output.”
The paper relies on this without support.
The ultimately responsible human interpreter of “What the machine says” is constantly faced with the need to make credibility judgments.
“The uhimately responsible human interpreter of “What the machine says” is, not unlike the correspondent with E H Z A , constantly faced with the need to make credibility judgments.”
The paper relies on this without support.
The act of writing a script is a kind of programming act and has all the advantages of programming, most particularly that it clearly shows where the programmer's understanding and command of his subject leaves off.
“The act of writing a script is a kind of programming act and has all the advantages of programming, most particularly that it clearly shows where the progranuner's understanding and con> mand of his subject leaves off.”
The paper relies on this without support.
The human speaker contributes much to clothe ELIZA's responses in plausibility.
“The human speaker will, a~ has been said, contribute much to clothe EI,IZA'S responses h: vestmen:s of plausibility.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ELIZA is a simple rules-based chatbot from the 1960's that users were found to anthropomorphize.
“The eponymous ELIZA is a simple rules-based chatbot from the 1960's that users were found to anthropomorphize [56].”
From People cannot distinguish GPT-4 from a human in a Turing test (2025) · cites this paper as [56]
The trend of overinterpreting computational technology has been present in discourse for decades and manifested in attribution of human-like qualities to ELIZA and anthropomorphisation of language used to describe AI algorithms.
“This trend has been present in the discourse for decades and has manifested itself in, for example, in the attribution of human-like qualities to the simple chatbot ELIZA (Weizenbaum, 1966;Berry, 2023) or in the anthropomorphisation of the language used to describe AI algorithms, which McDermott (1976) warned against half a century ago, and which remains problematic today (Shwartz, 2020).”
From There is no such thing as conscious artificial intelligence (2025) · cites this paper as Weizenbaum, 1966
Eliza revealed that humans are susceptible to projecting onto a chat bot an illusion of understanding.
“However, what Eliza did reveal was that humans are susceptible to projecting onto a chat bot an illusion of understanding, a valuable lesson that we should keep in mind.”
From Large Language Models and the Reverse Turing Test (2023) · cites this paper as Weizenbaum, 1966
ELIZA, developed in the 1960s, exemplified rule-based systems by simulating a Rogerian psychotherapist through simple pattern-matching and substitution rules.
“ELIZA, developed in the 1960s, exemplified these systems by simulating a Rogerian psychotherapist through simple pattern‐matching and substitution rules”
From Charting the evolution of artificial intelligence mental health chatbots from rule‐based systems to large language models: a systematic review (2025) · cites this paper as [10]
Data have been gathered from independent authors, labs, and across time all illustrating the utility and potential of machines in delivering healthcare and therapy.
“To Turing’s point, data have been gathered from independent authors, labs, and across time all illustrating the utility and potential of machines in delivering healthcare and therapy [2, 4, 6–8].”
From When ELIZA meets therapists: a Turing test for the heart and mind (2025) · cites this paper as [2]
The pattern of accurate identification being only marginally better than chance is consistent with the original hypothesis and previous research that differences in accurate identification will be close to zero.
“This pattern of findings is consistent with our original hypothesis and previous research: differences in accurate identification will be close to zero [2, 7, 8].”
From When ELIZA meets therapists: a Turing test for the heart and mind (2025) · cites this paper as [2]
Some potentially relevant conversational agents developed for the treatment of mental health problems were excluded from this review because of not reporting a mental health outcome measure.
“Furthermore, some potentially relevant conversational agents developed for the treatment of mental health problems were excluded from this review because of not reporting a mental health outcome measure (eg, ELIZA [2,42-44]).”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [2]
The majority of the identified literature reviews focus on studies from 1998 onward, thereby neglecting first papers on CAs such as ELIZA.
“The majority of reviews focus on studies from 1998 onward, thereby neglecting first papers on CAs such as well-known first text-based agent ELIZA (Weizenbaum, 1966).”
From Charting the Evolution and Future of Conversational Agents: A Research Agenda Along Five Waves and New Frontiers (2024) · cites this paper as Weizenbaum, 1966
The first chatbot was developed in 1966 by Weizenbaum.
“Historically, the first chatbot was developed in 1966 by Weizenbaum (1966).”
From Charting the Evolution and Future of Conversational Agents: A Research Agenda Along Five Waves and New Frontiers (2024) · cites this paper as Weizenbaum (1966)
Cites
Weizenbaum, J. (1963) · Communications of the ACM · doi:10.1145/367593.367617 · Cited by 196 · see citing works
Self-citationComputer Simulation of Individual Belief Systems
Abelson, R. P., & Carroll, J. D. (1965) · American Behavioral Scientist · doi:10.1177/000276426500800908 · Cited by 108 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
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A Survey of the Usages of Deep Learning for Natural Language Processing
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“The first well-established chatbot of this kind—ELIZA—was programmed in 1966 to simulate a text-based conversation with a psychotherapist.(2)”
MethodGenerative AI and ChatGPT: applications, challenges, and AI-human collaboration
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An Overview of Chatbot Technology
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Artificial intelligence and communication: A Human–Machine Communication research agenda
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History of artificial intelligence in medicine
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Chatbots: History, technology, and applications
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“There are many anecdotes, going all the way back to ELIZA (Weizenbaum, 1966), of systems that managed to fool their users into”
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“Early chatbots utilized either keyword matching mechanisms (Weizenbaum, 1966) or natural language processing mechanisms (Melián-González et al., 2021).”
MethodAI in CAI: An Artificial-Intelligence Approach to Computer-Assisted Instruction
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Interacting with educational chatbots: A systematic review
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The design and implementation of Xiaoice, an empathetic social chatbot
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From Eliza to XiaoIce: Challenges and opportunities with social chatbots
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“Nevertheless, many users believed they were talking to a real person when Eliza first came out.”
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This computer responds to user frustration:
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Conversational Agents in Health Care: Scoping Review and Conceptual Analysis
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“The first conversational agent ELIZA was developed by Weizenbaum [12] in 1966, with ELIZA taking on the role of a person-centered Rogerian psychotherapist (Figure 1).”
MethodA review of opportunities and challenges of chatbots in education
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A Survey on Conversational Recommender Systems
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Artificial intelligence—A personal view
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The DARPA Twitter Bot Challenge
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“Historical traces of AI’s foray into education can be linked to the development of early chat-bots in the 1960s (Weizenbaum, 1966).”
A Theoretical Framework for Conversational Search
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Artificially intelligent chatbots in digital mental health interventions: a review
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“In particular, ELIZA [21] and A.L.I.C.E. [22] were well-known early chatbot systems simulating real human communication.”
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"I Hear You, I Feel You": Encouraging Deep Self-disclosure through a Chatbot
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- Authors
- Weizenbaum, J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Weizenbaum, Joseph
- Venue
- Communications of the ACM
- Published
- 1966-01-01
- Volume
- 9
- Issue
- 1
- Pages
- 36-45
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W4236521339
- Cited by
- 4,452
- References
- 2
- Citations by year
- 2026: 450 · 2025: 576 · 2024: 524 · 2023: 465 · 2022: 298 · 2021: 310 · 2020: 245 · 2019: 231 · 2018: 197 · 2017: 103 · 2016: 109 · 2015: 63 · 2014: 100 · 2013: 75 · 2012: 44
- FWCI
- 10.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- Funders
- Office of Naval Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- Eliza, natural language conversation, human-computer interaction, decomposition rules, response generation, script editing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-28.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-28.
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