When A Man Says He Is Pregnant: ERP Evidence for A Rational Account of Speaker-contextualized Language Comprehension

Spoken language is often, if not always, understood in a context that includes the identities of speakers. For instance, we can easily make sense of an utterance such as "I'm going to have a manicure this weekend" or "The first time I got pregnant I had a hard time" when the utterance is spoken by a woman, but it would be harder to understand when it is spoken by a man. Previous event-related potential (ERP) studies have shown mixed results regarding the neurophysiological responses to such speaker-mismatched utterances, with some reporting an N400 effect and others a P600 effect. In an experiment involving 64 participants, we showed that these different ERP effects reflect distinct cognitive processes employed to resolve the speaker-message mismatch. When possible, the message is integrated with the speaker context to arrive at an interpretation, as in the case of violations of social stereotypes (e.g., men getting a manicure), resulting in an N400 effect. However, when such integration is impossible due to violations of biological knowledge (e.g., men getting pregnant), listeners engage in an error correction process to revise either the perceived utterance or the speaker context, resulting in a P600 effect. Additionally, we found that the social N400 effect decreased as a function of the listener's personality trait of openness, while the biological P600 effect remained robust. Our findings help to reconcile the empirical inconsistencies in the literature and provide a rational account of speaker-contextualized language comprehension.

Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability

As large language models (LLMs) become advance in their linguistic capacity, understanding how they capture aspects of language competence remains a significant challenge. This study therefore employs psycholinguistic paradigms, which are well-suited for probing deeper cognitive aspects of language processing, to explore neuron-level representations in language model across three tasks: sound-shape association, sound-gender association, and implicit causality. Our findings indicate that while GPT-2-XL struggles with the sound-shape task, it demonstrates human-like abilities in both sound-gender association and implicit causality. Targeted neuron ablation and activation manipulation reveal a crucial relationship: when GPT-2-XL displays a linguistic ability, specific neurons correspond to that competence; conversely, the absence of such an ability indicates a lack of specialized neurons. This study is the first to utilize psycholinguistic experiments to investigate deep language competence at the neuron level, providing a new level of granularity in model interpretability and insights into the internal mechanisms driving language ability in transformer based LLMs.

HLB: Benchmarking LLMs' Humanlikeness in Language Use

As synthetic data becomes increasingly prevalent in training language models, particularly through generated dialogue, concerns have emerged that these models may deviate from authentic human language patterns, potentially losing the richness and creativity inherent in human communication. This highlights the critical need to assess the humanlikeness of language models in real-world language use. In this paper, we present a comprehensive humanlikeness benchmark (HLB) evaluating 20 large language models (LLMs) using 10 psycholinguistic experiments designed to probe core linguistic aspects, including sound, word, syntax, semantics, and discourse (see https://huggingface.co/spaces/XufengDuan/HumanLikeness). To anchor these comparisons, we collected responses from over 2,000 human participants and compared them to outputs from the LLMs in these experiments. For rigorous evaluation, we developed a coding algorithm that accurately identified language use patterns, enabling the extraction of response distributions for each task. By comparing the response distributions between human participants and LLMs, we quantified humanlikeness through distributional similarity. Our results reveal fine-grained differences in how well LLMs replicate human responses across various linguistic levels. Importantly, we found that improvements in other performance metrics did not necessarily lead to greater humanlikeness, and in some cases, even resulted in a decline. By introducing psycholinguistic methods to model evaluation, this benchmark offers the first framework for systematically assessing the humanlikeness of LLMs in language use.

Grammaticality Representation in ChatGPT as Compared to Linguists and Laypeople

Large language models (LLMs) have demonstrated exceptional performance across various linguistic tasks. However, it remains uncertain whether LLMs have developed human-like fine-grained grammatical intuition. This preregistered study (https://osf.io/t5nes) presents the first large-scale investigation of ChatGPT's grammatical intuition, building upon a previous study that collected laypeople's grammatical judgments on 148 linguistic phenomena that linguists judged to be grammatical, ungrammatical, or marginally grammatical (Sprouse, Schutze, & Almeida, 2013). Our primary focus was to compare ChatGPT with both laypeople and linguists in the judgement of these linguistic constructions. In Experiment 1, ChatGPT assigned ratings to sentences based on a given reference sentence. Experiment 2 involved rating sentences on a 7-point scale, and Experiment 3 asked ChatGPT to choose the more grammatical sentence from a pair. Overall, our findings demonstrate convergence rates ranging from 73% to 95% between ChatGPT and linguists, with an overall point-estimate of 89%. Significant correlations were also found between ChatGPT and laypeople across all tasks, though the correlation strength varied by task. We attribute these results to the psychometric nature of the judgment tasks and the differences in language processing styles between humans and LLMs.

Does ChatGPT resemble humans in language use?

Large language models (LLMs) and LLM-driven chatbots such as ChatGPT have shown remarkable capacities in comprehending and producing language. However, their internal workings remain a black box in cognitive terms, and it is unclear whether LLMs and chatbots can develop humanlike characteristics in language use. Cognitive scientists have devised many experiments that probe, and have made great progress in explaining, how people process language. We subjected ChatGPT to 12 of these experiments, pre-registered and with 1,000 runs per experiment. In 10 of them, ChatGPT replicated the human pattern of language use. It associated unfamiliar words with different meanings depending on their forms, continued to access recently encountered meanings of ambiguous words, reused recent sentence structures, reinterpreted implausible sentences that were likely to have been corrupted by noise, glossed over errors, drew reasonable inferences, associated causality with different discourse entities according to verb semantics, and accessed different meanings and retrieved different words depending on the identity of its interlocutor. However, unlike humans, it did not prefer using shorter words to convey less informative content and it did not use context to disambiguate syntactic ambiguities. We discuss how these convergences and divergences may occur in the transformer architecture. Overall, these experiments demonstrate that LLM-driven chatbots like ChatGPT are capable of mimicking human language processing to a great extent, and that they have the potential to provide insights into how people learn and use language.