United States

We study some Large Language Models to explore their deficiencies in resolving sense ambiguities. In this connection, we evaluate their performance on well-known word sense disambiguation datasets. Word Sense Disambiguation (WSD) has been a long-standing NLP problem, which has given rise to many evaluation datasets and models over the decades. Recently the emergence of Large Language Models (LLM) raises much hope in improving accuracy. In this work, we evaluate word sense disambiguation capabilities of four LLMs: OpenAI’s ChatGPT-3.5, Mistral’s 7b parameter model, Meta’s Llama 70b, and Google’s Gemini Pro. We evaluate many well-established datasets containing a variety of texts and senses on these. After observing the performances of some datasets, we selectively study some failure cases and identify the reasons for failures. We explore human judgments that would correct these failures. Our findings suggest that many failure cases are related to a lack of world knowledge and the reasoning to amalgamate this knowledge rather than the lack of linguistic knowledge. We categorize the judgments so that the next generation of LLMs can improve by incorporating deeper world knowledge and reasoning. We conclude that word sense disambiguation could serve as a guide for probing the reasoning power of LLMs to measure their functional competency. We also list the accuracy of these datasets. We find that on many occasions, accuracy drops to below 70%, which is much less than that of well-performing existing models.

EMNLP 2024

On Functional Competence of LLMs for Linguistic Disambiguation

cognitive theory

word sense disambiguation

computational linguistics

large language models

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EMNLP 2022

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Extracting adverse reactions to medications or treatments is a crucial activity in the biomedical domain. The task involves identifying mentions of drugs and their adverse effects/events in raw text, which is challenging due to the unstructured nature of clinical narratives. In this paper, we propose TpT-ADE, a novel joint twophase transformer model combined with natural language processing (NLP) techniques, to identify adverse events (AEs) caused by drugs. In the first phase of TpT-ADE, entities are extracted and are grounded with their standard terms using the Unified Medical Language System (UMLS) knowledge base. In the second phase, entity and relation classification is performed to determine the presence of a relationship between the drug and AE pairs. TpT-ADE also identifies the intensity of AE entities by constructing a parts-of-speech (POS) embedding model. Unlike previous approaches that use complex classifiers, TpT-ADE employs a shallow neural network and yet outperforms the state-of-the-art methods on the standard ADE corpus.

TpT-ADE: Transformer Based Two-Phase ADE Extraction

Recent advances in computational linguistics include simulating the emergence of human-like languages with interacting neural network agents, starting from sets of random symbols. The recently introduced NeLLCom framework (Lian et al., 2023) allows agents to first learn an artificial language and then use it to communicate, with the aim of studying the emergence of specific linguistics properties. We extend this framework (NeLLCom-X) by introducing more realistic role-alternating agents and group communication in order to investigate the interplay between language learnability, communication pressures, and group size effects. We validate NeLLCom-X by replicating key findings from prior research simulating the emergence of a word-order/case-marking trade-off. Next, we investigate how interaction affects linguistic convergence and emergence of the trade-off. The novel framework facilitates future simulations of diverse linguistic aspects, emphasizing the importance of interaction and group dynamics in language evolution.

NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication

We introduce the Principled Reasoning and Acting (PRAct) framework, a novel LLM agent for learning and enforcing action principles from trajectory data.

PRACT: Optimizing Principled Reasoning and Acting of LLM Agent

We study attribute control in language models through the method of Causal Average Treatment Effect (Causal ATE). Existing methods for the attribute control task in Language Models (LMs) check for the co-occurrence of words in a sentence with the attribute of interest, and control for them. However, spurious correlation of the words with the attribute in the training dataset, can cause models to hallucinate the presence of the attribute when presented with the spurious correlate during inference. We show that the simple perturbation-based method of Causal ATE removes this unintended effect. Specifically, we ground it in the problem of toxicity mitigation, where a significant challenge lies in the inadvertent bias that often emerges towards protected groups post detoxification. We show that this unintended bias can be solved by the use of the Causal ATE metric. We provide experimental validations for our claims and release our code (anonymously) here: \href{https://github.com/causalate-mitigates-bias/causal-ate-mitigates-bias}{github.com/causalate-mitigates-bias/causal-ate-mitigates-bias}.

Causal ATE Mitigates Unintended Bias in Controlled Text Generation

Text Segmentation (TS) is the task of segmenting bodies of text into coherent blocks, mostly defined by the topics each segment contains. Historically, techniques in this area have been unsupervised, with more success recently coming from supervised methods instead. Although these approaches see better performance, they require training data and upfront training time. We propose a new method called Coherence, where we use sentence embeddings to pull representational keywords as the main constructor of sentences when comparing them to one another. Additionally, we include a storage of previously found keywords for the purposes of creating a more accurate segment representation instead of just the immediate sentence in question. We show improved results over current state-of-the-art unsupervised techniques when analyzed using Pk and WindowDiff scores. Coherence also requires no fine-tuning.

Words That Stick: Using Keyword Cohesion to Improve Text Segmentation

English relative clauses are a critical test case for theories of syntactic processing. Expectation- and memory-based accounts make opposing predictions, and behavioral experiments have found mixed results. We present a technical extension of Lossy Context Surprisal (LCS) and use it to model relative clause processing in three behavioral experiments. LCS predicts key results at distinct retention rates, showing that task-dependent memory demands can account for discrepant behavioral patterns in the literature.

Lossy Context Surprisal Predicts Task-Dependent Patterns in Relative Clause Processing

Language Models (LMs) excel in natural language processing tasks for English but show reduced performance in most other languages. This problem is commonly tackled by continually pre-training and fine-tuning these models for said languages. A significant issue in this process is the limited vocabulary coverage in the original model’s tokenizer, leading to inadequate representation of new languages and necessitating an expansion of the tokenizer. The initialization of the embeddings corresponding to new vocabulary items presents a further challenge. Current strategies require cross-lingual embeddings and lack a solid theoretical foundation as well as comparisons with strong baselines. In this paper, we first establish theoretically that initializing within the convex hull of existing embeddings is a good initialization, followed by a novel but simple approach, Constrained Word2Vec (CW2V), which does not require cross-lingual embeddings. Our study evaluates different initialization methods for expanding RoBERTa and LLaMA 2 across four languages and five tasks. The results show that CW2V performs equally well or even better than more advanced techniques. Additionally, simpler approaches like multivariate initialization perform on par with these advanced methods indicating that efficient large-scale multilingual continued pretraining can be achieved even with simpler initialization methods.

An Empirical Comparison of Vocabulary Expansion and Initialization Approaches For Language Models

Hierarchical text classification (HTC) is the task of assigning labels to a text within a structured space organized as a hierarchy. Recent works treat HTC as a conventional multilabel classification problem, therefore evaluating it as such. We instead propose to evaluate models based on specifically designed hierarchical metrics and we demonstrate the intricacy of metric choice and prediction inference method. We introduce a new challenging dataset and we evaluate fairly, recent sophisticated models, comparing them with a range of simple but strong baselines, including a new theoretically motivated loss. Finally, we show that those baselines are very often competitive with the latest models. This highlights the importance of carefully considering the evaluation methodology when proposing new methods for HTC.

Revisiting Hierarchical Text Classification: Inference and Metrics

Structural priming is a widely used psycholinguistic paradigm to study human sentence representations. In this work we introduce SPAWN, a cognitively motivated parser that can generate quantitative priming predictions from contemporary theories in syntax which assume a lexicalized grammar. By generating and testing priming predictions from competing theoretical accounts, we can infer which assumptions from syntactic theory are useful for characterizing the representations humans build when processing sentences. As a case study, we use SPAWN to generate priming predictions from two theories (Whiz-Deletion and Participial-Phase) which make different assumptions about the structure of English relative clauses. By modulating the reanalysis mechanism that the parser uses and strength of the parser's prior knowledge, we generated nine sets of predictions from each of the two theories. Then, we tested these predictions using a novel web-based comprehension-to-production priming paradigm. We found that while the some of the predictions from the Participial-Phase theory aligned with human behavior, none of the predictions from the the Whiz-Deletion theory did, thus suggesting that the Participial-Phase theory might better characterize human relative clause representations.

SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser

This paper evaluates whether large language models (LLMs) exhibit cognitive fan effects, similar to those discovered by Anderson in humans, after being pre-trained on human textual data. We conduct two sets of in-context recall experiments designed to elicit fan effects. Consistent with human results, we find that LLM recall uncertainty, measured via token probability, is influenced by the fan effect. Our results show that removing uncertainty disrupts the observed effect. The experiments suggest the fan effect is consistent whether the fan value is induced in-context or in the pre-training data. Finally, these findings provide in-silico evidence that fan effects and typicality are expressions of the same phenomena.

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