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Found 32 papers, 18 papers with code
Syntactic Learnability of Echo State Neural Language Models at Scale
What is a neural model with minimum architectural complexity that exhibits reasonable language learning capability?
Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis of L1-Dependent Biases
This study evaluates Large Language Models' (LLMs) ability to simulate non-native-like English use observed in human second language (L2) learners interfered with by their native first language (L1).
On Representational Dissociation of Language and Arithmetic in Large Language Models
The association between language and (non-linguistic) thinking ability in humans has long been debated, and recently, neuroscientific evidence of brain activity patterns has been considered.
Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability
To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety.
Can Input Attributions Interpret the Inductive Reasoning Process Elicited in In-Context Learning?
Elucidating the rationale behind neural models' outputs has been challenging in the machine learning field, which is indeed applicable in this age of large language models (LLMs) and in-context learning (ICL).
Think-to-Talk or Talk-to-Think? When LLMs Come Up with an Answer in Multi-Step Arithmetic Reasoning
This study investigates the internal reasoning process of language models during arithmetic multi-step reasoning, motivated by the question of when they internally form their answers during reasoning.
First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning
Multi-step reasoning instruction, such as chain-of-thought prompting, is widely adopted to explore better language models (LMs) performance.
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data.
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese
Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages.
Emergent Word Order Universals from Cognitively-Motivated Language Models
The world's languages exhibit certain so-called typological or implicational universals; for example, Subject-Object-Verb (SOV) languages typically use postpositions.
Psychometric Predictive Power of Large Language Models
In other words, pure next-word probability remains a strong predictor for human reading behavior, even in the age of LLMs.
Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism
Large language models (LLMs) take advantage of step-by-step reasoning instructions, e. g., chain-of-thought (CoT) prompting.
Second Language Acquisition of Neural Language Models
With the success of neural language models (LMs), their language acquisition has gained much attention.
Transformer Language Models Handle Word Frequency in Prediction Head
Prediction head is a crucial component of Transformer language models.
Empirical Investigation of Neural Symbolic Reasoning Strategies
Neural reasoning accuracy improves when generating intermediate reasoning steps.
Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?
Compositionality is a pivotal property of symbolic reasoning.
Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
Transformers are ubiquitous in wide tasks.
Does Vision Accelerate Hierarchical Generalization in Neural Language Learners?
This study explores the advantage of grounded language acquisition, specifically the impact of visual information -- which humans can usually rely on but LMs largely do not have access to during language acquisition -- on syntactic generalization in LMs.
Context Limitations Make Neural Language Models More Human-Like
Language models (LMs) have been used in cognitive modeling as well as engineering studies -- they compute information-theoretic complexity metrics that simulate humans' cognitive load during reading.
Instance-Based Neural Dependency Parsing
Interpretable rationales for model predictions are crucial in practical applications.
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models
Transformer architecture has become ubiquitous in the natural language processing field.
Lower Perplexity is Not Always Human-Like
Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.
Modeling Event Salience in Narratives via Barthes' Cardinal Functions
Events in a narrative differ in salience: some are more important to the story than others.
Langsmith: An Interactive Academic Text Revision System
Despite the current diversity and inclusion initiatives in the academic community, researchers with a non-native command of English still face significant obstacles when writing papers in English.
Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese
We examine a methodology using neural language models (LMs) for analyzing the word order of language.
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Interpretable rationales for model predictions play a critical role in practical applications.
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms
Attention is a key component of Transformers, which have recently achieved considerable success in natural language processing.
Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance
The writing process consists of several stages such as drafting, revising, editing, and proofreading.
TEASPN: Framework and Protocol for Integrated Writing Assistance Environments
Language technologies play a key role in assisting people with their writing.
An Empirical Study of Span Representations in Argumentation Structure Parsing
For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established.
Feasible Annotation Scheme for Capturing Policy Argument Reasoning using Argument Templates
Most of the existing works on argument mining cast the problem of argumentative structure identification as classification tasks (e. g. attack-support relations, stance, explicit premise/claim).
