Richard L. Lewis

Also published as: Richard Lewis


pdf bib
In-Context Analogical Reasoning with Pre-Trained Language Models
Xiaoyang Hu | Shane Storks | Richard Lewis | Joyce Chai
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Analogical reasoning is a fundamental capacity of human cognition that allows us to reason abstractly about novel situations by relating them to past experiences. While it is thought to be essential for robust reasoning in AI systems, conventional approaches require significant training and/or hard-coding of domain knowledge to be applied to benchmark tasks. Inspired by cognitive science research that has found connections between human language and analogy-making, we explore the use of intuitive language-based abstractions to support analogy in AI systems. Specifically, we apply large pre-trained language models (PLMs) to visual Raven’s Progressive Matrices (RPM), a common relational reasoning test. By simply encoding the perceptual features of the problem into language form, we find that PLMs exhibit a striking capacity for zero-shot relational reasoning, exceeding human performance and nearing supervised vision-based methods. We explore different encodings that vary the level of abstraction over task features, finding that higher-level abstractions further strengthen PLMs’ analogical reasoning. Our detailed analysis reveals insights on the role of model complexity, in-context learning, and prior knowledge in solving RPM tasks.


pdf bib
Accounting for Agreement Phenomena in Sentence Comprehension with Transformer Language Models: Effects of Similarity-based Interference on Surprisal and Attention
Soo Hyun Ryu | Richard Lewis
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

We advance a novel explanation of similarity-based interference effects in subject-verb and reflexive pronoun agreement processing, grounded in surprisal values computed from a pretrained large-scale Transformer model, GPT-2. Specifically, we show that surprisal of the verb or reflexive pronoun predicts facilitatory interference effects in ungrammatical sentences, where a distractor noun that matches in number with the verb or pronouns leads to faster reading times, despite the distractor not participating in the agreement relation. We review the human empirical evidence for such effects, including recent meta-analyses and large-scale studies. We also show that attention patterns (indexed by entropy and other measures) in the Transformer show patterns of diffuse attention in the presence of similar distractors, consistent with cue-based retrieval models of parsing. But in contrast to these models, the attentional cues and memory representations are learned entirely from the simple self-supervised task of predicting the next word.


pdf bib
A Modeling Study of the Effects of Surprisal and Entropy in Perceptual Decision Making of an Adaptive Agent
Pyeong Whan Cho | Richard Lewis
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Processing difficulty in online language comprehension has been explained in terms of surprisal and entropy reduction. Although both hypotheses have been supported by experimental data, we do not fully understand their relative contributions on processing difficulty. To develop a better understanding, we propose a mechanistic model of perceptual decision making that interacts with a simulated task environment with temporal dynamics. The proposed model collects noisy bottom-up evidence over multiple timesteps, integrates it with its top-down expectation, and makes perceptual decisions, producing processing time data directly without relying on any linking hypothesis. Temporal dynamics in the task environment was determined by a simple finite-state grammar, which was designed to create the situations where the surprisal and entropy reduction hypotheses predict different patterns. After the model was trained to maximize rewards, the model developed an adaptive policy and both surprisal and entropy effects were observed especially in a measure reflecting earlier processing.


pdf bib
Dynamic encoding of structural uncertainty in gradient symbols
Pyeong Whan Cho | Matthew Goldrick | Richard L. Lewis | Paul Smolensky
Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018)


pdf bib
Computationally Rational Saccadic Control: An Explanation of Spillover Effects Based on Sampling from Noisy Perception and Memory
Michael Shvartsman | Richard Lewis | Satinder Singh
Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics


pdf bib
Modeling Sentence Processing in ACT-R
Shravan Vasishth | Richard L. Lewis
Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together