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Found 8 papers, 5 papers with code
A Simple Unsupervised Approach for Coreference Resolution using Rule-based Weak Supervision
Labeled data for the task of Coreference Resolution is a scarce resource, requiring significant human effort.
Groundedness in Retrieval-augmented Long-form Generation: An Empirical Study
We present an empirical study of groundedness in long-form question answering (LFQA) by retrieval-augmented large language models (LLMs).
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
We find evidence that LLMs, with and without instruction-tuning, exhibit human-like biases in both the text-comprehension and the solution-planning steps of the solving process, but not during the final step which relies on the problem's arithmetic expressions (solution execution).
Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models
We show that MechanisticProbe is able to detect the information of the reasoning tree from the model's attentions for most examples, suggesting that the LM indeed is going through a process of multi-step reasoning within its architecture in many cases.
A Mechanistic Interpretation of Arithmetic Reasoning in Language Models using Causal Mediation Analysis
Mathematical reasoning in large language models (LMs) has garnered significant attention in recent work, but there is a limited understanding of how these models process and store information related to arithmetic tasks within their architecture.
Distilling Reasoning Capabilities into Smaller Language Models
In this work, we propose an alternative reasoning scheme, Socratic CoT, that learns a decomposition of the original problem into a sequence of subproblems and uses it to guide the intermediate reasoning steps.
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models
By grounding the behavioral analysis in a causal graph describing an intuitive reasoning process, we study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space.
Longtonotes: OntoNotes with Longer Coreference Chains
Ontonotes has served as the most important benchmark for coreference resolution.
