LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond
With the recent appearance of LLMs in practical settings, having methods that
can effectively detect factual inconsistencies is crucial to reduce the
propagation of misinformation and improve trust in model outputs. When testing
on existing factual consistency benchmarks, we find that a few large language
models (LLMs) perform competitively on classification benchmarks for factual
inconsistency detection compared to traditional non-LLM methods. However, a
closer analysis reveals that most LLMs fail on more complex formulations of the
task and exposes issues with existing evaluation benchmarks, affecting
evaluation precision. To address this, we propose a new protocol for
inconsistency detection benchmark creation and implement it in a 10-domain
benchmark called SummEdits. This new benchmark is 20 times more cost-effective
per sample than previous benchmarks and highly reproducible, as we estimate
inter-annotator agreement at about 0.9. Most LLMs struggle on SummEdits, with
performance close to random chance. The best-performing model, GPT-4, is still
8\% below estimated human performance, highlighting the gaps in LLMs' ability
to reason about facts and detect inconsistencies when they occur.
646edd6566f7b97a94feb3c1
Philippe Laban
claimed_verified
2023-05-25T15:20:57.688Z
610aee2dc30263ce69e0dd9c
/avatars/b6e5bba78425e9c28d1b75ee8a7eaac1.svg
Philippe Laban
user
philippelaban
646edd6566f7b97a94feb3c2
Wojciech Kryściński
646edd6566f7b97a94feb3c3
Divyansh Agarwal
646edd6566f7b97a94feb3c4
Alexander R. Fabbri
646edd6566f7b97a94feb3c5
Caiming Xiong
646edd6566f7b97a94feb3c6
Shafiq Joty
646edd6566f7b97a94feb3c7
Chien-Sheng Wu
claimed_verified
2023-05-26T08:07:29.023Z
5f09073719cb630495b81428
/avatars/e49f53128b5305dfec44d80129c59ca3.svg
Chien-Sheng (Jason) Wu
user
jasonwu
LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond
With the recent appearance of LLMs in practical settings, having methods that
can effectively detect factual inconsistencies is crucial to reduce the
propagation of misinformation and improve trust in model outputs. When testing
on existing factual consistency benchmarks, we find that a few large language
models (LLMs) perform competitively on classification benchmarks for factual
inconsistency detection compared to traditional non-LLM methods. However, a
closer analysis reveals that most LLMs fail on more complex formulations of the
task and exposes issues with existing evaluation benchmarks, affecting
evaluation precision. To address this, we propose a new protocol for
inconsistency detection benchmark creation and implement it in a 10-domain
benchmark called SummEdits. This new benchmark is 20 times more cost-effective
per sample than previous benchmarks and highly reproducible, as we estimate
inter-annotator agreement at about 0.9. Most LLMs struggle on SummEdits, with
performance close to random chance. The best-performing model, GPT-4, is still
8\% below estimated human performance, highlighting the gaps in LLMs' ability
to reason about facts and detect inconsistencies when they occur.
A new benchmark called SummEdits shows that most large language models struggle to detect factual inconsistencies effectively, highlighting gaps in their reasoning abilities.
LLMs factual consistency benchmarks SummEdits inter-annotator agreement