LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanation

• URL: http://arxiv.org/abs/2510.05490v1
• Date: Tue, 07 Oct 2025 01:10:02 GMT
• Title: LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanation
• Authors: Zhoutong Fu, Yihan Cao, Yi-Lin Chen, Aman Lunia, Liming Dong, Neha Saraf, Ruijie Jiang, Yun Dai, Qingquan Song, Tan Wang, Guoyao Li, Derek Koh, Haichao Wei, Zhipeng Wang, Aman Gupta, Chengming Jiang, Jianqiang Shen, Liangjie Hong, Wenjing Zhang,
• Abstract summary: At LinkedIn, job person fit task requires analyzing a candidate's public profile against job requirements to produce both a fit assessment and a detailed explanation.<n>We introduce LANTERN, a novel LLM knowledge distillation framework tailored specifically for job person fit tasks.<n>We show that LANTERN significantly improves task specific metrics for both job person fit and explanation.
• Score: 16.960316035628008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have achieved strong performance across a wide range of natural language processing tasks. However, deploying LLMs at scale for domain specific applications, such as job-person fit and explanation in job seeking platforms, introduces distinct challenges. At LinkedIn, the job person fit task requires analyzing a candidate's public profile against job requirements to produce both a fit assessment and a detailed explanation. Directly applying open source or finetuned LLMs to this task often fails to yield high quality, actionable feedback due to the complexity of the domain and the need for structured outputs. Moreover, the large size of these models leads to high inference latency and limits scalability, making them unsuitable for online use. To address these challenges, we introduce LANTERN, a novel LLM knowledge distillation framework tailored specifically for job person fit tasks. LANTERN involves modeling over multiple objectives, an encoder model for classification purpose, and a decoder model for explanation purpose. To better distill the knowledge from a strong black box teacher model to multiple downstream models, LANTERN incorporates multi level knowledge distillation that integrates both data and logit level insights. In addition to introducing the knowledge distillation framework, we share our insights on post training techniques and prompt engineering, both of which are crucial for successfully adapting LLMs to domain specific downstream tasks. Extensive experimental results demonstrate that LANTERN significantly improves task specific metrics for both job person fit and explanation. Online evaluations further confirm its effectiveness, showing measurable gains in job seeker engagement, including a 0.24\% increase in apply rate and a 0.28\% increase in qualified applications.

Related papers

• Knowledge Graphs for Enhancing Large Language Models in Entity Disambiguation [0.061446808540639365]
  We use Knowledge Graphs to enhance Large Language Models (LLMs) for zero-shot Entity Disambiguation (ED)<n>We leverage the hierarchical representation of the entities' classes in a KG to prune the candidate space as well as the entities' descriptions to enrich the input prompt with additional factual knowledge.<n>Our evaluation on popular ED datasets shows that the proposed method outperforms non-enhanced and description-only enhanced LLMs, and has a higher degree of adaptability than task-specific models.
  arXiv  Detail & Related papers  (2025-05-05T15:40:24Z)
• Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework [81.29965270493238]
  We develop a specialized dataset aimed at enhancing the evaluation and fine-tuning of large language models (LLMs) for wireless communication applications.<n>The dataset includes a diverse set of multi-hop questions, including true/false and multiple-choice types, spanning varying difficulty levels from easy to hard.<n>We introduce a Pointwise V-Information (PVI) based fine-tuning method, providing a detailed theoretical analysis and justification for its use in quantifying the information content of training data.
  arXiv  Detail & Related papers  (2025-01-16T16:19:53Z)
• Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning [0.0]
  We develop a robust pipeline to identify variables such as remote work availability, remuneration structures, educational requirements, and work experience preferences.<n>Our methodology combines semantic chunking, retrieval-augmented generation (RAG), and fine-tuning DistilBERT models to overcome the limitations of traditional parsing tools.<n>We present a comprehensive evaluation of our fine-tuned models and analyze their strengths, limitations, and potential for scaling.
  arXiv  Detail & Related papers  (2025-01-13T19:49:49Z)
• Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
  Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data. We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
  arXiv  Detail & Related papers  (2024-10-31T06:55:24Z)
• Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback [52.763620660061115]
  ONI is a distributed architecture that simultaneously learns an RL policy and an intrinsic reward function.<n>We explore a range of algorithmic choices for reward modeling with varying complexity.<n>Our approach achieves state-of-the-art performance across a range of challenging tasks from the NetHack Learning Environment.
  arXiv  Detail & Related papers  (2024-10-30T13:52:43Z)
• Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models [22.676688441884465]
  Fine-tuning pre-trained large language models (LLMs) on a diverse array of tasks has become a common approach for building models. This study investigates the task-specific information encoded in pre-trained LLMs and the effects of instruction tuning on their representations.
  arXiv  Detail & Related papers  (2024-10-25T23:38:28Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• LLM Augmented LLMs: Expanding Capabilities through Composition [56.40953749310957]
  CALM -- Composition to Augment Language Models -- introduces cross-attention between models to compose their representations and enable new capabilities. We illustrate that augmenting PaLM2-S with a smaller model trained on low-resource languages results in an absolute improvement of up to 13% on tasks like translation into English. When PaLM2-S is augmented with a code-specific model, we see a relative improvement of 40% over the base model for code generation and explanation tasks.
  arXiv  Detail & Related papers  (2024-01-04T18:53:01Z)
• Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations [50.81844184210381]
  We propose a general paradigm that augments large language models with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE. This paradigm relies on a domain knowledge extractor, working in three steps: 1) preparing effective knowledge for the task; 2) selecting the knowledge for each specific sample; and 3) expressing the knowledge in an LLM-understandable way.
  arXiv  Detail & Related papers  (2023-11-16T07:09:38Z)
• Graph Neural Prompting with Large Language Models [32.97391910476073]
  Graph Neural Prompting (GNP) is a novel plug-and-play method to assist pre-trained language models in learning beneficial knowledge from knowledge graphs. Extensive experiments on multiple datasets demonstrate the superiority of GNP on both commonsense and biomedical reasoning tasks.
  arXiv  Detail & Related papers  (2023-09-27T06:33:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.