REVIEW 7 cited by
Larimar: Large Language Models with Episodic Memory Control
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
Signed reviews
read the original abstract
Efficient and accurate updating of knowledge stored in Large Language Models (LLMs) is one of the most pressing research challenges today. This paper presents Larimar - a novel, brain-inspired architecture for enhancing LLMs with a distributed episodic memory. Larimar's memory allows for dynamic, one-shot updates of knowledge without the need for computationally expensive re-training or fine-tuning. Experimental results on multiple fact editing benchmarks demonstrate that Larimar attains accuracy comparable to most competitive baselines, even in the challenging sequential editing setup, but also excels in speed - yielding speed-ups of 8-10x depending on the base LLM - as well as flexibility due to the proposed architecture being simple, LLM-agnostic, and hence general. We further provide mechanisms for selective fact forgetting, information leakage prevention, and input context length generalization with Larimar and show their effectiveness. Our code is available at https://github.com/IBM/larimar
Forward citations
Cited by 7 Pith papers
-
ExAnte: A Benchmark for Ex-Ante Inference in Large Language Models
Models leak future knowledge despite explicit temporal cutoffs, as quantified by the ExAnte benchmark across four tasks.
-
What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents
KV-cache eviction, prompt compression, recurrent state bounding, and agent memory consolidation are unified as one rate-distortion problem with a shared lower bound, shared failure mode, and transferable mechanisms.
-
A Simple "Try Again" Can Elicit Multi-Turn LLM Reasoning
Multi-turn RL with only unary 'try again' feedback improves multi-turn reasoning accuracy by up to 14% while preserving single-turn performance.
-
GET: Goal-directed Exploration and Targeting for Large-Scale Unknown Environments
A framework coupling LLM direction proposals with an external evaluator and a Gaussian-mixture experience memory reduces object-search path length and time versus heuristic baselines in real-world large-scale robot trials.
-
Memorization and Knowledge Injection in Gated LLMs
MEGa injects episodic memories into separate gated LoRA adapters selected by embedding similarity, mitigating catastrophic forgetting and enabling recall, QA, and compositional questions on two datasets.
-
MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models
MIITA stores past supervised examples as compact hidden-space correction directions and applies retrieved directions through gated temporary hidden-state updates, improving continual learning in small language models ...
-
Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents
The authors propose episodic memory, with five defining properties, as the unifying framework needed for LLM agents to learn and remember over long time horizons.
Discussion (0). Continue with ORCID to comment.