Simon Willison
Qwen3.8 27B addition in words
Read the original on Simon Willison →The Flow has not summarised this story yet — read it at Simon Willison.
Related stories
arXiv Computation and Language
Oct 5
Writerslogic at the CLEF 2026 SimpleText Track: Multi-Candidate LLM Simplification and Stacked Complexity Spotting
The Writerslogic team participated in the CLEF 2026 SimpleText shared task, tackling both text simplification (Task 1) and complexity spotting (Task 2). For simplification, they built a multi‑candidate pipeline with GPT‑4o‑mini, selecting the best candidate via a reference‑free heuristic, and their Claude Sonnet 4 submission achieved a SARI of 47.43 and BLEU of 14.21, ranking third overall on the Task 1 leaderboard. For complexity spotting, they fine‑tuned a DeBERTa‑v3‑large NLI model on 350 K labeled pairs, achieving a macro F1 of 0.8081 (0.8085 in an ensemble) on binary over‑generation identification and 0.804 accuracy on multi‑class error classification, placing them second among unique teams.
arXiv Computer Vision
Oct 7
PlotPick: AI-powered batch extraction of numerical data from scientific figures
PlotPick is an open‑source tool that employs vision‑language models (VLMs) to batch‑extract tabular data from scientific figures. In benchmarks on synthetic chart datasets (ChartX and PlotQA), nine VLMs generally outperform the dedicated chart‑to‑table model DePlot, especially on box plots and horizontal bar charts. The tool’s figure detection, output formatting, and default model (Claude Haiku 4.5) were not evaluated, and accuracy on real biomedical figures remains unverified.
arXiv Machine Learning
Sep 22
Calibrated Fusion for Heterogeneous Graph-Vector Retrieval in Multi-Hop QA
arXiv:2603.28886v3 Announce Type: replace-cross Abstract: Graph-augmented retrieval combines dense similarity with graph-based relevance signals such as Personalized PageRank (PPR), but these scores...
arXiv Machine Learning
Sep 16
Script Fragmentation and Format: What Drives the English-Bengali Performance Gap in Open LLMs?
arXiv:2507.23248v2 Announce Type: replace-cross Abstract: Bengali is spoken by more than 230 million people, yet no standardized instrument evaluates large language models (LLMs) on Bengali across th...
Berkeley AI Research
Sep 1, 2025
What exactly does word2vec learn?
What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting language modeling task.
arXiv Computation and Language
Sep 17
Playing log(N)-Questions over Wikipedia Abstracts: Communication Efficiency Between Paired Frontier Models
The study evaluates six frontier language models on a two‑agent <log(N)>‑Questions game using Wikipedia lead paragraphs. In each game a questioner must identify a target paragraph with exactly <log2 N> yes/no questions, while an answerer only sees the target and the question and replies with a single word. Across 408 games, the models perform similarly, with Claude Opus 5 winning 28 of 68 games and the top five models showing only marginal differences; win rates decline sharply with larger document sets, following a reliability parameter of 0.928 per question. "whyItMatters":"The results reveal how well language models can communicate under information asymmetry, highlighting that even top models struggle to extract a full bit per question and that reasoning token usage does not strongly predict success."