1 citations · 1 across the 5 of their papers we have counts for
5 papers
FREESON: Retriever-Free Retrieval-Augmented Reasoning via Corpus-Traversing MCTS
Chaeeun Kim, Seungone Kim
Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in multi-step reasoning and calling search engines at appropriate steps. However, existing retrieval-augment…
LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation
Chaeeun Kim, Jinu Lee, Wonseok Hwang
Legal Case Retrieval (LCR), which retrieves relevant cases from a query case, is a fundamental task for legal professionals in research and decision-making. However, existing studi…
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
Seungone Kim, Juyoung Suk, Ji Yong Cho +29
As language models (LMs) become capable of handling a wide range of tasks, their evaluation is becoming as challenging as their development. Most generation benchmarks currently as…
How Well Do Large Language Models Truly Ground?
Hyunji Lee, Sejune Joo, Chaeeun Kim +4
To reduce issues like hallucinations and lack of control in Large Language Models (LLMs), a common method is to generate responses by grounding on external contexts given as input,…
Exploring the Practicality of Generative Retrieval on Dynamic Corpora
Chaeeun Kim, Soyoung Yoon, Hyunji Lee +3
Benchmarking the performance of information retrieval (IR) is mostly conducted with a fixed set of documents (static corpora). However, in realistic scenarios, this is rarely the c…