FlenQA
Flexible Length Question Answering dataset for evaluating the impact of input length on reasoning performance of language models, featuring True/False questions embedded in contexts of varying lengths (250-3000 tokens) across three reasoning tasks: Monotone Relations, People In Rooms, and simplified Ruletaker
Phi 4 Reasoning Plus from Microsoft currently leads the FlenQA leaderboard with a score of 0.979 across 2 evaluated AI models.
What FlenQA measures
FlenQA is a text benchmark that evaluates large language models on long context and reasoning tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 1.0, with the leader reaching 1.0.
Compare leaders on the best AI for long context and best AI for reasoning leaderboards.
Publication
- Paper
- Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
- Authors
- Mosh Levy, Alon Jacoby, Yoav Goldberg
- Published
- arXiv
- 2402.14848
Abstract
This paper explores the impact of extending input lengths on the capabilities of Large Language Models (LLMs). Despite LLMs advancements in recent times, their performance consistency across different input lengths is not well understood. We investigate this aspect by introducing a novel QA reasoning framework, specifically designed to assess the impact of input length. We isolate the effect of input length using multiple versions of the same sample, each being extended with padding of different lengths, types and locations. Our findings show a notable degradation in LLMs' reasoning performance at much shorter input lengths than their technical maximum. We show that the degradation trend appears in every version of our dataset, although at different intensities. Additionally, our study reveals that the traditional metric of next word prediction correlates negatively with performance of LLMs' on our reasoning dataset. We analyse our results and identify failure modes that can serve as useful guides for future research, potentially informing strategies to address the limitations observed in LLMs.
Phi 4 Reasoning Plus leads with 97.9%, followed by Phi 4 Reasoning at 97.7%.
Progress Over Time
Interactive timeline showing model performance evolution on FlenQA
FlenQA Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Microsoft | 14B | — | — | ||
| 2 | Microsoft | 14B | — | — |
FAQ
Common questions about FlenQA.
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