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Rigorously Assessing Natural Language Explanations of Neurons (2309.10312v1)

Published 19 Sep 2023 in cs.CL

Abstract: Natural language is an appealing medium for explaining how LLMs process and store information, but evaluating the faithfulness of such explanations is challenging. To help address this, we develop two modes of evaluation for natural language explanations that claim individual neurons represent a concept in a text input. In the observational mode, we evaluate claims that a neuron $a$ activates on all and only input strings that refer to a concept picked out by the proposed explanation $E$. In the intervention mode, we construe $E$ as a claim that the neuron $a$ is a causal mediator of the concept denoted by $E$. We apply our framework to the GPT-4-generated explanations of GPT-2 XL neurons of Bills et al. (2023) and show that even the most confident explanations have high error rates and little to no causal efficacy. We close the paper by critically assessing whether natural language is a good choice for explanations and whether neurons are the best level of analysis.

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A Rigorous Evaluation Framework for Natural Language Explanations of Neurons

The paper "Rigorously Assessing Natural Language Explanations of Neurons" addresses a critical challenge in the field of interpretability of LLMs—the evaluation of natural language explanations purportedly detailing the role of individual neurons in these models. The authors establish a clear framework for assessing these explanations through two evaluation modes: observational and intervention-based, both rigorously assessing the explanations' fidelity.

Overview of Evaluation Framework

The framework proposed in the paper delineates two distinct approaches to evaluate explanations that claim certain neurons represent specific concepts:

  1. Observational Evaluation: This mode examines whether a neuron's activations accord with the explanations given. It involves defining an explanation as a set of strings related to the concept it is supposed to represent. Observational evaluation tests whether neuron activations align accurately across these strings. The authors underscore the necessity to quantify precision and recall in this mode, thereby identifying both Type I and Type II errors in the explanations.
  2. Intervention-Based Evaluation: This mode uses causal relations to verify explanations. It assesses if a neuron functions as a causal mediator for the concept proposed by the explanation. By intervening on neuron's activations, they determine the extent to which neuron manipulation affects model behaviors associated with the concept in question.

Findings from Applying the Framework

The framework was applied to evaluate explanations generated by an automated method involving GPT-4, focusing on neurons in GPT-2~XL. Despite high confidence assigned by GPT-4 to these explanations, observational tests revealed deficiencies—the F1 score was 0.56, far from satisfactory, and decisively pointing to discrepancies between neuron activation predictions and actual activations.

Moreover, intervention-based evaluation revealed limited causal efficacy. Neurons, even when considered collectively, failed to mediate the concepts effectively as per their explanations, often exhibiting effects equivalent to those obtained by random neuron selection.

Implications for Future Research

The implications of these findings are significant for both theoretical understanding and practical applications. The paper propounds that while neurons may show encoding of relevant features, explanations often lack causal grounding. This poses a challenge for downstream tasks like model editing or bias mitigation, which rely on precise neuron-to-concept mappings.

From a theoretical standpoint, it suggests reevaluating natural language as the preferred medium for explanation. The inherent ambiguity and context dependence in language might lead to explanations that are not directly actionable for technical decision-making. Additionally, considering explanations beyond individual neurons might be beneficial, as model reasoning often involves distributed representations transcending single neurons.

Conclusion

This paper advocates for an empirical and rigorous approach to validate neuron-level interpretations within LLMs. By critiquing the effectiveness of explanations derived from heuristic LLMs, it casts a cautious glance toward emerging methods of interpretability. Addressing these findings, future research might focus on formalizing languages for explanations or exploring other hierarchical structures in models that might offer a more interpretable level of analysis beyond the confines of individual neurons.

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References (43)
  1. CEBab: Estimating the causal effects of real-world concepts on NLP model behavior. In Advances in Neural Information Processing Systems.
  2. Omer Antverg and Yonatan Belinkov. 2022. On the pitfalls of analyzing individual neurons in language models. In International Conference on Learning Representations.
  3. Faithfulness tests for natural language explanations. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 283–294, Toronto, Canada. Association for Computational Linguistics.
  4. Yonatan Belinkov. 2022. Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics, 48(1):207–219.
  5. Language models can explain neurons in language models. https://openaipublic.blob.core.windows.net/neuron-explainer/paper/index.html.
  6. Neural network attributions: A causal perspective. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 981–990. PMLR.
  7. Towards automated circuit discovery for mechanistic interpretability.
  8. e-snli-ve-2.0: Corrected visual-textual entailment with natural language explanations. CoRR, abs/2004.03744.
  9. Toy models of superposition. Transformer Circuits Thread.
  10. CausaLM: Causal model explanation through counterfactual language models. Computational Linguistics, 47(2):333–386.
  11. Causal abstractions of neural networks. In Advances in Neural Information Processing Systems.
  12. Causal abstraction for faithful model interpretation. Ms., Stanford University.
  13. Inducing causal structure for interpretable neural networks. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 7324–7338. PMLR.
  14. Finding alignments between interpretable causal variables and distributed neural representations. Ms., Stanford University.
  15. Dissecting recall of factual associations in auto-regressive language models.
  16. Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 30–45, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  17. Transformer feed-forward layers are key-value memories. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5484–5495, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  18. Generating visual explanations. In Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV, volume 9908 of Lecture Notes in Computer Science, pages 3–19. Springer.
  19. Natural language descriptions of deep features. In International Conference on Learning Representations.
  20. Inducing character-level structure in subword-based language models with type-level interchange intervention training. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12163–12180, Toronto, Canada. Association for Computational Linguistics.
  21. Ayush Kaushal and Kyle Mahowald. 2022. What do tokens know about their characters and how do they know it? In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2487–2507, Seattle, United States. Association for Computational Linguistics.
  22. Explaining chest x-ray pathologies in natural language. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part V, volume 13435 of Lecture Notes in Computer Science, pages 701–713. Springer.
  23. Textual explanations for self-driving vehicles. In Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part II, volume 11206 of Lecture Notes in Computer Science, pages 577–593. Springer.
  24. Program induction by rationale generation: Learning to solve and explain algebraic word problems. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pages 158–167. Association for Computational Linguistics.
  25. Disentangling visual and written concepts in clip. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16389–16398.
  26. Parallel Distributed Processing. Volume 2: Psychological and Biological Models. MIT Press, Cambridge, MA.
  27. Locating and editing factual associations in GPT. In Advances in Neural Information Processing Systems.
  28. Mass-editing memory in a transformer. In The Eleventh International Conference on Learning Representations.
  29. Language models implement simple word2vec-style vector arithmetic.
  30. Jesse Mu and Jacob Andreas. 2020. Compositional explanations of neurons. In Advances in Neural Information Processing Systems, volume 33, pages 17153–17163. Curran Associates, Inc.
  31. In-context learning and induction heads. Transformer Circuits Thread. Https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html.
  32. Barbara H Partee. 1995. Lexical semantics and compositionality. In Lila R. Gleitman and Mark Liberman, editors, Invitation to Cognitive Science, volume 1, pages 311–360. MIT Press, Cambridge, MA.
  33. Judea Pearl. 2014. Interpretation and identification of causal mediation. Psychological methods, 19.
  34. Christopher Potts and Roger Levy. 2015. Negotiating lexical uncertainty and speaker expertise with disjunction. In Proceedings of the 41st Annual Meeting of the Berkeley Linguistics Society, pages 417–445, Berkeley, CA. Berkeley Linguistics Society.
  35. Language models are unsupervised multitask learners. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf.
  36. Parallel Distributed Processing. Volume 1: Foundations. MIT Press, Cambridge, MA.
  37. Explaining black box text modules in natural language with language models.
  38. Paul Smolensky. 1988. On the proper treatment of connectionism. Behavioral and Brain Sciences, 11(1):1–23.
  39. Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 3319–3328. PMLR.
  40. Investigating gender bias in language models using causal mediation analysis. In Advances in Neural Information Processing Systems, volume 33, pages 12388–12401. Curran Associates, Inc.
  41. Interpretability in the wild: a circuit for indirect object identification in GPT-2 small. In The Eleventh International Conference on Learning Representations.
  42. BLiMP: The benchmark of linguistic minimal pairs for English. Transactions of the Association for Computational Linguistics, 8:377–392.
  43. Sarah Wiegreffe and Ana Marasovic. 2021. Teach me to explain: A review of datasets for explainable natural language processing. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1).
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Authors (5)
  1. Jing Huang (140 papers)
  2. Atticus Geiger (35 papers)
  3. Karel D'Oosterlinck (11 papers)
  4. Zhengxuan Wu (37 papers)
  5. Christopher Potts (113 papers)
Citations (21)
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