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GPT-3.5

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Headlines · 5

  1. Reddit re-run reports higher dual-NTA rates for newer Claude models

    A Reddit poster says a friend re-ran a Stanford AITA benchmark across 27 models, rewriting posts that Reddit judged “not the asshole” from the other participant’s perspective. The reported dual-NTA rate was 35% for Claude Sonnet 5, 30% for Claude Sonnet 5.5, and 14% for the earlier Claude Opus 4.1, while Grok 4.20 scored 73% and Gemini 4 Argon 7%. The poster’s claim that Claude is now the worst among its peers is stronger than the figures shown, and the results remain secondhand until independently reviewed.

    r/ClaudeAI · 🔥 0
  2. Stanford study finds many published LLM findings expire quickly

    The Decoder reports that Stanford researchers Owen Queen and James Zou tested 50 widely cited LLM claims across 20 models from eight developers. They found that 44% of the claims had expired, another 34% had weakened significantly, and only 16% remained supported, with 6% strengthening; among claims with estimated expiration dates, the average lifespan was 15.7 months. Results varied sharply by model, and the researchers advised treating empirical LLM findings as observations tied to specific models, versions and dates rather than general truths.

    The Decoder · 🔥 0
  3. Networked LLM Agents Form Polarized Opinion Groups

    A study by researchers from Tsinghua University and partner institutions, published in Nature Communications, built a network of 1,000 LLM agents and tested GPT-3.5, GPT-4o, ChatGLM, Llama-3 and DeepSeek-V3. Without prescribed behavioral rules, agents formed opposing opinion groups, increasingly connected with similar-minded agents and showed self-inconsistency between their stated views, generated reasons and shared information. The researchers released code and quantified the effects of five interventions. In the polarized system, network-level changes had limited effects, while eliminating confirmation bias and neutral elite signals reduced polarization by 11.8% and 8.8%, respectively. A 36Kr article was an authorized republication of the New Zhiyuan report, not independent confirmation. The researchers cautioned that the simulation does not predict real-world populations and may be affected by the selected models, prompts and simplified opinion representation.

    新智元 · 🔥 0
  4. Legora uses OpenAI models and Codex for agentic legal workflows

    OpenAI says Legora uses OpenAI models, ChatGPT, and Codex across its legal AI platform, including agentic legal work, contract analysis, and large-scale document review. Legora’s Tabular Review workflow can analyze thousands of contracts across multiple questions in minutes, according to the company. OpenAI also reports that one Fortune 100 customer reduced contract turnaround time by 90%, saved more than $20 million on external litigation and diligence, and returned 9.5 hours per lawyer each week; these figures are company claims.

    OpenAI · 🔥 0
  5. Only 11 of 26 popular Chinese 'de-AI-flavor' rules survive testing

    A hospital worker who writes science explainers in his spare time reviewed 13 Chinese open-source 'de-AI-flavor' writing projects and found most of their rule lists had never been validated: only lieflat-less-ai-tone had tested anything, having five models write 60 pieces each (300 AI articles) and comparing them with 329 human-written public articles (~2.83 million characters), scoring 26 circulating criteria — just 11 held up, while 15 were refuted, including sentence-length uniformity. The author then built a Claude Code skill that tiers rules by evidence and adds hard constraints such as information conservation (every fact in the final draft must exist in the source) and no changing the strength of claims, plus a scanning script that uses only the Python standard library. On the Chinese HC3 dataset, 3.4% of 12,000-plus human answers were flagged as AI-like, versus 18.9% for GPT-3.5 answers and 85% for Claude answers and 83% for 12 science drafts.

    少数派 AI · 🔥 0

Experience and discussion from the community

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Swarm Intelligence•Florian•Opinion

LLMs: Two Paths to a Leap

When asking AI to solve a problem, we usually prepare the question, rules, and evaluation criteria for it in advance. Scientific research often starts from an earlier point: how should a phenomenon be described, which premise in an existing theory deserves scrutiny, and what kind of new explanation could connect several things that were previously separate. After reasoning capabilities continue to…

LLMInnovationDeductionInductionAbduction

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