REVIEW 7 cited by
Foundations of Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book is structured into five main chapters, each exploring a key area: pre-training, generative models, prompting, alignment, and inference. It is intended for college students, professionals, and practitioners in natural language processing and related fields, and can serve as a reference for anyone interested in large language models.
Forward citations
Cited by 7 Pith papers
-
Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs
Benign PEFT fine-tuning changes LLM safety and fairness: adapter-based methods (LoRA, IA3) preserve alignment better than prompt-based methods, and the base model strongly moderates outcomes.
-
Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details
For Other-Play in Yokai, agents trained with different implementation details coordinate across implementations about as well as across seeds, supporting inter-seed cross-play as a proxy for cross-implementation evaluation.
-
SasAgent: Multi-Agent AI System for Small-Angle Scattering Data Analysis
SasAgent connects a large language model to SasView tools through four agents, letting users calculate SLDs, generate synthetic scattering curves, and fit experimental SAS data from text prompts.
-
Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMs
RevBrowse retrieves preference-relevant pros and cons from reviews via a contrastively trained module, then uses an LLM to rerank candidates; experiments on four Amazon datasets show consistent improvements over baselines.
-
Generating Privacy Stories From Software Documentation
LLMs can extract privacy behaviors from software documents and draft privacy stories, but the best overall F1 is 0.766, not the abstract's 0.8+.
-
HEAL: A Hypothesis-Based Preference-Aware Analysis Framework
HEAL evaluates preference optimization by measuring ranking accuracy and strength correlation between model likelihoods and proxy reward scores over multi-response hypothesis spaces.
-
Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models
A step-level verifier-guided hybrid of Best-of-N sampling, Monte Carlo tree search, and conditional self-refinement improves reasoning in small instruction-tuned LLMs, claiming up to 28.6-point gains.
Discussion (0). Sign in to comment.