Binary Retrieval-Augmented Reward Mitigates Hallucinations

Posted3 months agoActive3 months ago

MarlonPro

44 points

3 comments

arxiv.orgResearchstory

calmpositive

Debate

20/100

AI ResearchHallucination MitigationReward EngineeringNatural Language Processing

Key topics

AI Research

Hallucination Mitigation

Reward Engineering

Natural Language Processing

Researchers propose a binary retrieval-augmented reward method to mitigate hallucinations in AI models, sparking interest and discussion on its potential applications and implications.

Snapshot generated from the HN discussion

Discussion Activity

Light discussion

First comment

Peak period

3-4h

Avg / period

1.5

Key moments

01Story posted
Oct 21, 2025 at 12:14 PM EDT
3 months ago
Step 01
02First comment
Oct 21, 2025 at 2:43 PM EDT
2h after posting
Step 02
03Peak activity
2 comments in 3-4h
Hottest window of the conversation
Step 03
04Latest activity
Oct 21, 2025 at 3:27 PM EDT
3 months ago
Step 04

Generating AI Summary...

Analyzing up to 500 comments to identify key contributors and discussion patterns

Discussion (3 comments)

Showing 3 comments

amflare

3 months ago

2 replies

> Existing mitigation approaches often degrade performance on open-ended generation and downstream tasks, limiting their practical utility. [...] Unlike continuous reward schemes, our approach assigns a reward of one only when the model's output is entirely factually correct, and zero otherwise.

Someone correct me if I am wrong, as I'm am on the very edge of this space looking in, but does this mean that they are using a "degraded performance with fewer hallucinations" model to fact check the "more powerful yet prone to hallucinations" model?

svnt

3 months ago

Also on the edge, but it appears they are relying on the search-augmented identification of conflicts in the generated statement, which is an easier task than constructing an answer to the question. It also encourages abstention because there are no conflicts in “I don’t know” (so “mitigating hallucinations” and “answering more questions correctly” are not necessarily the same thing)

mNovak

3 months ago

My understanding is no, they are collecting a cache of documents from the training set, then after pre-training prompt about those topics. A separate verifier is given both the relevant source documents and generated response, and tasked with checking for conflicts in factuality.

They describe using Qwen 32B as the verifier, and the model under training is Qwen 8B. So in fact the verifier is beefier than the trainee model, though it's unclear if that has to be the case as you scale up.

View full discussion on Hacker News

ID: 45657595Type: storyLast synced: 11/20/2025, 2:27:16 PM

Want the full context?

Jump to the original sources

Read the primary article or dive into the live Hacker News thread when you're ready.

Open link View on HN