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Testing the limits of natural language models for predicting human language judgements

Nature Machine Intelligence volume 5, pages 952–964 (2023)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

Neural network language models appear to be increasingly aligned with how humans process and generate language, but identifying their weaknesses through adversarial examples is challenging due to the discrete nature of language and the complexity of human language perception. We bypass these limitations by turning the models against each other. We generate controversial sentence pairs where two language models disagree about which sentence is more likely to occur. Considering nine language models (including n-gram, recurrent neural networks and transformers), we created hundreds of controversial sentence pairs through synthetic optimization or by selecting sentences from a corpus. Controversial sentence pairs proved highly effective at revealing model failures and identifying models that aligned most closely with human judgements of which sentence is more likely. The most human-consistent model tested was GPT-2, although experiments also revealed substantial shortcomings in its alignment with human perception.

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Fig. 1: Model comparison using natural sentences.
Fig. 2: Synthesizing controversial sentence pairs.
Fig. 3: Model comparison using synthetic sentences.
Fig. 4: Ordinal correlation of the models’ sentence probability log ratios and human Likert ratings.

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Data availability

The experimental stimuli, detailed behavioural testing results and code for reproducing all analyses and figures are available at github.com/dpmlab/contstimlang (ref. 67).

Code availability

Sentence optimization code is available at github.com/dpmlab/contstimlang (ref. 67).

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Acknowledgements

This material is based on work partially supported by the National Science Foundation under grant no. 1948004 to N.K. This publication was made possible with the support of the Charles H. Revson Foundation (to T.G.). The statements made and views expressed, however, are solely the responsibility of the authors.

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Author notes

  1. These authors contributed equally: Tal Golan, Matthew Siegelman.

Authors and Affiliations

  1. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA

    Tal Golan & Nikolaus Kriegeskorte

  2. Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Be’er-Sheva, Israel

    Tal Golan

  3. Department of Psychology, Columbia University, New York, NY, USA

    Matthew Siegelman, Nikolaus Kriegeskorte & Christopher Baldassano

  4. Department of Neuroscience, Columbia University, New York, NY, USA

    Nikolaus Kriegeskorte

  5. Department of Electrical Engineering, Columbia University, New York, NY, USA

    Nikolaus Kriegeskorte

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Contributions

T.G., M.S., N.K. and C.B. designed the study. M.S. implemented the computational models and T.G. implemented the sentence pair optimization procedures. M.S. conducted the behavioural experiments. T.G. and M.S. analysed the experiments’ results. T.G., M.S., N.K. and C.B. wrote the paper.

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Correspondence to Tal Golan.

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Extended data

Extended Data Fig. 1 An example of one experimental trial, as presented to the participants.

The participant must choose one sentence while providing their confidence rating on a 3-point scale.

Extended Data Fig. 2 Between-model agreement rate on the probability ranking of the 90 randomly sampled and paired natural sentence pairs evaluated in the experiment.

Each cell represents the proportion of sentence pairs for which two models make congruent probability ranking (that is, both models assign a higher probability to sentence 1, or both models assign a higher probability to sentence 2).

Extended Data Fig. 3 Pairwise model comparison of model-human consistency.

For each pair of models (represented as one cell in the matrices above), the only trials considered were those in which the stimuli were either selected (a) or synthesized (b) to contrast the predictions of the two models. For these trials, the two models always made controversial predictions (that is, one sentence is preferred by the first model and the other sentence is preferred by the second model). The matrices above depict the proportion of trials in which the binarized human judgments aligned with the row model (‘model 1’). For example, GPT-2 (top-row) was always more aligned (green hues) with the human choices than its rival models. In contrast, 2-gram (bottom-row) was always less aligned (purple hues) with the human choices than its rival models.

Extended Data Fig. 4 Pairwise model analysis of human response for natural vs. synthetic sentence pairs.

In each optimization condition, a synthetic sentence s was formed by modifying a natural sentence n so the synthetic sentence would be ‘rejected’ by one model (mreject, columns), minimizing p(s∣mreject), and would be ‘accepted’ by another model (maccept, rows), satisfying the constraint p(s∣maccept)≥p(n∣maccept). Each cell above summarizes model-human agreement in trials resulting from one such optimization condition. The color of each cell denotes the proportion of trials in which humans judged a synthetic sentence to be more likely than its natural counterpart and hence aligned with maccept. For example, the top-right cell depicts human judgments for sentence pairs formed to minimize the probability assigned to the synthetic sentence by the simple 2-gram model while ensuring that GPT-2 would judge the synthetic sentence to be at least as likely as the natural sentence; humans favored the synthetic sentence in only 22 out the 100 sentence pairs in this condition.

Extended Data Fig. 5 Human consistency of bidirectional transformers: approximate log-likelihood versus pseudo-log-likelihood (PLL).

Each dot in the plots above depicts the ordinal correlation between the judgments of one participant and the predictions of one model. (a) The performance of BERT, RoBERTa, and ELECTRA in predicting the human judgments of randomly sampled natural sentence pairs in the main experiment, using two different likelihood measures: our novel approximate likelihood method (that is, averaging multiple conditional probability chains, see Methods) and pseudo-likelihood (PLL, summating the probability of each word given all of the other words64). For each model, we statistically compared the two likelihood measures to each other and to the noise ceiling using a two-sided Wilcoxon signed-rank test across the participants. False discovery rate was controlled at q < 0.05 for the 9 comparisons. When predicting human preferences of natural sentences, the pseudo-log-likelihood measure is at least as accurate as our proposed approximate log-likelihood measure. (b) Results from a follow-up experiment, in which we synthesized synthetic sentence pairs for each of the model pairs, pitting the two alternative likelihood measures against each other. Statistical testing was conducted in the same fashion as in panel a. These results indicate that for each of the three bidirectional language models, the approximate log-likelihood measure is considerably and significantly (q < 0.05) more human-consistent than the pseudo-likelihood measure. Synthetic controversial sentence pairs uncover a dramatic failure mode of the pseudo-log-likelihood measure, which remains covert when the evaluation is limited to randomly-sampled natural sentences. See Extended Data Table 2 for synthetic sentence pair examples.

Extended Data Fig. 6 Model prediction accuracy for pairs of natural and synthetic sentences, evaluating each model across all of the sentence pairs in which it was targeted to rate the synthetic sentence to be less probable than the natural sentence.

The data binning applied here is complementary to the one used in Fig. 3b, where each model was evaluated across all of the sentence pairs in which it was targeted to rate the synthetic sentence to be at least as probable as the natural sentence. Unlike Fig. 3b, where all of the models performed poorly, here no models were found to be significantly below the lower bound on the noise ceiling; typically, when a sentence was optimized to decrease its probability under any model (despite the sentence probability not decreasing under a second model), humans agreed that the sentence became less probable.

Extended Data Table 1 Examples of pairs of synthetic and natural sentences that maximally contributed to each model’s prediction error
Full size table
Extended Data Table 2 Examples of controversial synthetic-sentence pairs that maximally contributed to the prediction error of bidirectional transformers using pseudo-log-likelihood (PLL)
Full size table

Supplementary information

Supplementary Information

Supplementary methods 1.1–1.3, results 2.1–2.3, Figs. 1–3 and Table 1.

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Golan, T., Siegelman, M., Kriegeskorte, N. et al. Testing the limits of natural language models for predicting human language judgements. Nat Mach Intell 5, 952–964 (2023). https://doi.org/10.1038/s42256-023-00718-1

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