|
--- |
|
title: Optillm |
|
emoji: 💬 |
|
colorFrom: yellow |
|
colorTo: purple |
|
sdk: gradio |
|
sdk_version: 4.36.1 |
|
app_file: app.py |
|
pinned: false |
|
license: apache-2.0 |
|
--- |
|
|
|
## References |
|
|
|
- [Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation](https://arxiv.org/abs/2409.12941) |
|
- [Writing in the Margins: Better Inference Pattern for Long Context Retrieval](https://www.arxiv.org/abs/2408.14906) |
|
- [Chain-of-Thought Reasoning Without Prompting](https://arxiv.org/abs/2402.10200) |
|
- [Re-Reading Improves Reasoning in Large Language Models](https://arxiv.org/abs/2309.06275) |
|
- [In-Context Principle Learning from Mistakes](https://arxiv.org/abs/2402.05403) |
|
- [Planning In Natural Language Improves LLM Search For Code Generation](https://arxiv.org/abs/2409.03733) |
|
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) |
|
- [Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers](https://arxiv.org/abs/2408.06195) |
|
- [Mixture-of-Agents Enhances Large Language Model Capabilities](https://arxiv.org/abs/2406.04692) |
|
- [Prover-Verifier Games improve legibility of LLM outputs](https://arxiv.org/abs/2407.13692) |
|
- [Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning](https://arxiv.org/abs/2405.00451) |
|
- [Unsupervised Evaluation of Code LLMs with Round-Trip Correctness](https://arxiv.org/abs/2402.08699) |
|
- [Patched MOA: optimizing inference for diverse software development tasks](https://arxiv.org/abs/2407.18521) |
|
- [Patched RTC: evaluating LLMs for diverse software development tasks](https://arxiv.org/abs/2407.16557) |
