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Found 15 papers, 8 papers with code
Predicting Reference: What do Language Models Learn about Discourse Models?
Whereas there is a growing literature that probes neural language models to assess the degree to which they have latently acquired grammatical knowledge, little if any research has investigated their acquisition of discourse modeling ability.
Measuring Risk of Bias in Biomedical Reports: The RoBBR Benchmark
To draw reliable conclusions, these systems should take into account the quality of available evidence, placing more weight on studies that use a valid methodology.
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation
On average, our models demonstrate a 28. 18% improvement in answer accuracy and a 13. 89% increase in tool usage precision across all datasets, surpassing state-of-the-art models including GPT-4o and Claude-3. 5.
ClimaQA: An Automated Evaluation Framework for Climate Foundation Models
However, a critical issue remains: the lack of a comprehensive evaluation framework capable of assessing the quality and scientific validity of model outputs.
Dissociation of Faithful and Unfaithful Reasoning in LLMs
Through analysis of error recovery behaviors, we find evidence for unfaithfulness in Chain of Thought, which occurs when models arrive at the correct answer despite invalid reasoning text.
IR2: Information Regularization for Information Retrieval
This approach, representing a novel application of regularization techniques in synthetic data creation for IR, is tested on three recent IR tasks characterized by complex queries: DORIS-MAE, ArguAna, and WhatsThatBook.
BIRCO: A Benchmark of Information Retrieval Tasks with Complex Objectives
We present the Benchmark of Information Retrieval (IR) tasks with Complex Objectives (BIRCO).
DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries
In scientific research, the ability to effectively retrieve relevant documents based on complex, multifaceted queries is critical.
Systematic Generalization with Edge Transformers
Recent research suggests that systematic generalization in natural language understanding remains a challenge for state-of-the-art neural models such as Transformers and Graph Neural Networks.
Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention
We present a model that jointly learns the denotations of words together with their groundings using a truth-conditional semantics.
Word Frequency Does Not Predict Grammatical Knowledge in Language Models
Neural language models learn, to varying degrees of accuracy, the grammatical properties of natural languages.
Speakers enhance contextually confusable words
Recent work has found evidence that natural languages are shaped by pressures for efficient communication {---} e. g. the more contextually predictable a word is, the fewer speech sounds or syllables it has (Piantadosi et al. 2011).
Constraint-based Learning of Phonological Processes
Phonological processes are context-dependent sound changes in natural languages.
Grammar Induction for Minimalist Grammars using Variational Bayesian Inference : A Technical Report
The following technical report presents a formal approach to probabilistic minimalist grammar parameter estimation.
