Can a Crow Hatch a Falcon? Lineage Matters in Predicting Large Language Model Performance

Accurately forecasting the performance of Large Language Models (LLMs) before extensive fine-tuning or merging can substantially reduce both computational expense and development time. Although prior approaches like scaling laws account for global factors such as parameter size or training tokens, they often overlook explicit lineage relationships - i.e., which models are derived or merged from which parents. In this work, we propose a novel Lineage-Regularized Matrix Factorization (LRMF) framework that encodes ancestral ties among LLMs via a graph Laplacian regularizer. By leveraging multi-hop parent-child connections, LRMF consistently outperforms conventional matrix factorization and collaborative filtering methods in both instance-level and benchmark-level performance prediction. Our large-scale study includes 2,934 publicly available Hugging Face models and 21,000+ instances across 6 major benchmarks, showing that lineage constraints yield up to 7-10 percentage points higher correlation with actual performance compared to baselines. Moreover, LRMF effectively addresses the cold-start problem, providing accurate estimates for newly derived or merged models even with minimal data. This lineage-guided strategy thus offers a resource-efficient way to inform hyperparameter tuning, data selection, and model combination in modern LLM development.

LightPAL: Lightweight Passage Retrieval for Open Domain Multi-Document Summarization

Open-Domain Multi-Document Summarization (ODMDS) is crucial for addressing diverse information needs, which aims to generate a summary as answer to user's query, synthesizing relevant content from multiple documents in a large collection. Existing approaches that first find relevant passages and then generate a summary using a language model are inadequate for ODMDS. This is because open-ended queries often require additional context for the retrieved passages to cover the topic comprehensively, making it challenging to retrieve all relevant passages initially. While iterative retrieval methods have been explored for multi-hop question answering (MQA), they are impractical for ODMDS due to high latency from repeated large language model (LLM) inference for reasoning. To address this issue, we propose LightPAL, a lightweight passage retrieval method for ODMDS that constructs a graph representing passage relationships using an LLM during indexing and employs random walk instead of iterative reasoning and retrieval at inference time. Experiments on ODMDS benchmarks show that LightPAL outperforms baseline retrievers in summary quality while being significantly more efficient than an iterative MQA approach.

DeepJoin: Joinable Table Discovery with Pre-trained Language Models

Due to the usefulness in data enrichment for data analysis tasks, joinable table discovery has become an important operation in data lake management. Existing approaches target equi-joins, the most common way of combining tables for creating a unified view, or semantic joins, which tolerate misspellings and different formats to deliver more join results. They are either exact solutions whose running time is linear in the sizes of query column and target table repository or approximate solutions lacking precision. In this paper, we propose Deepjoin, a deep learning model for accurate and efficient joinable table discovery. Our solution is an embedding-based retrieval, which employs a pre-trained language model (PLM) and is designed as one framework serving both equi- and semantic joins. We propose a set of contextualization options to transform column contents to a text sequence. The PLM reads the sequence and is fine-tuned to embed columns to vectors such that columns are expected to be joinable if they are close to each other in the vector space. Since the output of the PLM is fixed in length, the subsequent search procedure becomes independent of the column size. With a state-of-the-art approximate nearest neighbor search algorithm, the search time is logarithmic in the repository size. To train the model, we devise the techniques for preparing training data as well as data augmentation. The experiments on real datasets demonstrate that by training on a small subset of a corpus, Deepjoin generalizes to large datasets and its precision consistently outperforms other approximate solutions'. Deepjoin is even more accurate than an exact solution to semantic joins when evaluated with labels from experts. Moreover, when equipped with a GPU, Deepjoin is up to two orders of magnitude faster than existing solutions.