Does Similarity Matter? The Case of Answer Extraction from Technical Discussion Forums

Challenges for Information Extraction from Dialogue in Criminal Law

Information extraction and question answering have the potential to introduce a new paradigm for how machine learning is applied to criminal law. Existing approaches generally use tabular data for predictive metrics. An …

Taxonomy Beats Corpus in Similarity Identification, but Does It Matter?

Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts

Modern reasoning language models generate dense, sequential chain-of-thought traces implicitly assuming that every token contributes and that steps must be consumed in order. We challenge both assumptions through a syste…

Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering

Continual learning is a promising alternative to the current pretrain-and-finetune paradigm: It aims to learn a model on a sequence of tasks without forgetting knowledge from preceding tasks. We investigate continual lea…

Finding Answers in Thought Matters: Revisiting Evaluation on Large Language Models with Reasoning

Evaluating generative models, such as large language models (LLMs), commonly involves question-answering tasks where the final answer is selected based on probability of answer choices. On the other hand, for models requ…