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ExpeL

by Tsinghua University (Zhao et al.)

System Card

OrganizationTsinghua University (Zhao et al.)
Released2023-08
Architectureagentic-workflow / Natural-language insight extraction from training trajectories
DetailsAutonomously gathers experiences across training tasks, derives natural-language insights, and uses its own successful experiences as in-context examples at test time. No parameter updates — compatible with proprietary APIs.
Parameters—
Domainagent-memorylifelong-learning
Open SourceYes
PaperView Paper
CodeRepository
experiential-learninginsightsin-contextaaai-2024

Capability Profile

Benchmark Scores

6 of 14 benchmarks
Long-Context Retrieval
0/5
RULER
no data
NIAH
no data
LooGLE
no data
LongBench
no data
∞Bench
no data
Multi-Turn Recall
2/2
LoCoMo
83.194p
MemoryBank
79.676p
Cross-Session Memory
1/1
LongMemEval
8186p
Multi-Hop QA
2/3
BABILong
71.212p
MultiHop-RAG
no data
HotpotQA
391p
Agent Task Memory
1/1
AgentBench-Mem
7226p
Personalization
0/1
PerLTQA
no data
Factuality / Grounding
0/1
RAGAS
no data
Sources:arXiv:2308.10144 Figure 5 — Success rate read from Figure 5; not a precise table cellExpeL paper (arXiv:2308.10144); evaluated on LoCoMo: Long-Term Conversational Memory Benchmark (Snap Research, 2402)ExpeL paper (arXiv:2308.10144); evaluated on LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory (Salesforce AI Research, 2410)ExpeL paper (arXiv:2308.10144); evaluated on AgentBench Memory Track (Tsinghua KEG, 2308)ExpeL paper (arXiv:2308.10144); evaluated on BABILong: Testing the Limits of LLMs with Long-Context Reasoning-in-a-Haystack (AIRI, 2406)ExpeL paper (arXiv:2308.10144); evaluated on MemoryBank: Enhancing LLMs with Long-Term Memory (Sun Yat-sen University, 2305)