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reddit-0c5f4300·10 events·first seen Aliases: Reddit
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PolymarketHindcastREDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution EditingWhisperToxiREX: A Dataset on Toxic REasoning in ConteXtToxiREXLatent Profile AnalysisSelf-Stigma Is Not a Monolith, but Generic Empathy Is: Persona-Conditioned LLM Support for People Who Use DrugsDo LLM Embedding Spaces Recover Expert Structure?Qwen3Beyond Accuracy: Community Perspectives on Machine TranslationSQUAREFRANZLLUMIDirect Preference Optimization (DPO)GPTsupervised fine-tuningChatGPTOpenAIReddit Data API
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Hindcast: A benchmark framework that closes data-leakage loopholes in LLM forecasting evaluation
A new arXiv preprint introduces Hindcast, an evaluation framework for LLM forecasters that addresses two systematic data-leakage channels in standard backtesting: post-event retrieval and training-data contamination from newer models. The system replays resolved Polymarket prediction markets against a frozen Reddit snapshot, grading models only on information available before a chosen cutoff date and comparing against contemporaneous market prices as a human-forecast baseline. Key finding: retrieval helps forecasting only when pre-event Reddit discussion existed; where only speculation was available, retrieval hurts performance. The framework is designed to remain valid as new models and markets emerge without going stale.
REDDIT framework corrects timestamp drift in autoregressive ASR without catastrophic forgetting
A new arXiv paper introduces REDDIT (Replay-based Distribution EDITing), a two-stage post-training framework that corrects timestamp drift in autoregressive ASR systems like Whisper across long non-speech spans. The method updates only 1.6% of model parameters and constructs correction supervision without human annotations, using VAD-trimmed speech with inserted non-speech gaps. On Whisper-tiny, long-gap mIoU improves from 38.7% to 95.0% and out-of-domain alignment error drops from 2752 ms to 223 ms, while preserving transcription quality that ordinary SFT decoder tuning catastrophically degrades.
ToxiREX: Multilingual contextual dataset for implicit toxicity detection with structured reasoning schema
Researchers introduce ToxiREX, a multilingual Reddit-based dataset for detecting implicit and context-dependent toxicity across six languages (English, Arabic, Turkish, Spanish, German, Dutch), anchored to real-world events like the 2023 Turkey earthquakes and the Russian invasion of Ukraine. The dataset includes 125K LLM-annotated training comments and ~3K human-annotated test comments, structured using a toxic reasoning schema that captures implicit toxicity and maps to existing taxonomies. Baseline results from prompted and fine-tuned language models show above-random but substantially suboptimal performance, indicating the task remains challenging. ToxiREX is claimed as the first dataset combining multilingual coverage, conversational context, and implicit toxicity with schema-based structured annotations.
Persona-conditioned LLM support for people who use drugs reveals tension between generic empathy and clinical alignment
Researchers present a proof-of-concept study using Latent Profile Analysis on Reddit data to identify four self-stigma personas among people who use drugs, then train classifiers to detect these personas from posting history (macro-F1 = 0.74 at 30 posts). Persona-matched LLM responses achieved targeted behavioral shifts, but clinical expert raters preferred the generic empathy of persona-neutral baselines. The core finding is a misalignment: holistic empathy judgments and clinically-aligned response design can pull in opposite directions, suggesting current evaluation rubrics for LLM-based mental health support are inadequate.
LLM embedding spaces partially recover expert-defined symptom structure in mental health language
A new arXiv preprint investigates whether LLM embedding geometry aligns with expert-defined symptom structure in mental health language, using 28 Reddit communities as a testbed. The authors compare pretrained and fine-tuned Qwen3 embeddings (0.6B and 4B) against an expert symptom matrix via representational similarity analysis, with controls for affective, stylistic, and topic confounds. Results show measurable but level-dependent alignment: fine-tuning strengthens it at fine-grained category levels, and larger scale improves both zero-shot alignment and fine-tuning gains. The paper argues that classification accuracy alone is insufficient to validate embedding geometry against domain knowledge.
Large-scale social media analysis reveals stakeholder conflicts over machine translation priorities
Researchers analyze 79,286 social media posts from Reddit, Facebook, Bluesky, and Mastodon (2019–2025) to compare how four communities—AI developers, professional translators, language learners, and language service providers—discuss machine translation. The study finds significant disagreements and polarized sentiments across groups, with AI researchers framing MT as a technical benchmark problem while non-AI users prioritize quality nuances, trust, reliability, and social concerns. The work argues for redirecting MT research toward community-identified needs rather than benchmark performance alone.
FRANZ: A Communicative Audit Framework for LLM Response Framing on Subjective Questions
Researchers introduce FRANZ, an automated framework for auditing how LLMs frame responses to subjective, culturally-sensitive questions across four dimensions: cultural positioning, generalizing language, anthropomorphic cues, and conversational maxims. The work is paired with SQUARE, a 376k-question corpus drawn from 57 subreddits and mapped to 7 countries and 19 question categories. Applying FRANZ to three open-weight LLMs reveals statistically significant differences in framing behavior, and uncovers a positive coupling between insider positioning and anthropomorphism that varies by country. The study argues that existing evaluations focused on factual correctness miss important communicative dimensions of LLM outputs.
LLUMI: Fine-Tuning Open-Source LLMs for Mental Health Writing Assistance Using Reddit Community Feedback
LLUMI is a two-component system (a generation model and an improvement model) designed to provide mental health writing assistance using smaller open-source LLMs hosted in privacy-preserving, on-premise environments. The system leverages Reddit community endorsement signals (upvotes/downvotes) to construct preference pairs for SFT and DPO training, then further aligns outputs via human evaluation across readability, empathy, connection, actionability, and safety dimensions. Results show LLUMI achieves performance comparable to proprietary GPT-based models on linguistic and human evaluations, suggesting community-derived preference signals can substitute for expensive expert labeling in sensitive domains.
OpenAI and Reddit Partnership
OpenAI has announced a partnership with Reddit to integrate Reddit's content into ChatGPT and other OpenAI products. The deal grants OpenAI access to Reddit's Data API, providing a large corpus of real-time and historical human-generated content. This follows a pattern of AI labs securing data licensing agreements ahead of potential regulatory scrutiny over training data sourcing.
AI Chatbots Are Giving Out People's Real Phone Numbers
Reports are emerging of individuals receiving misdirected calls and messages because generative AI systems, including Google's AI, are surfacing incorrect or misattributed phone numbers in response to user queries. Affected users describe weeks of unwanted contact from strangers seeking unrelated services. The issue highlights a concrete real-world harm from AI hallucination or data contamination in deployed consumer products.