10,149 research outputs found
Speculative Contrastive Decoding
Large language models (LLMs) have shown extraordinary performance in various
language tasks, but high computational requirements hinder their widespread
deployment. Speculative decoding, which uses amateur models to predict the
generation of expert models, has been proposed as a way to accelerate LLM
inference. However, speculative decoding focuses on acceleration instead of
making the best use of the token distribution from amateur models. We proposed
Speculative Contrastive Decoding (SCD), an accelerated decoding method
leveraging the natural contrast between expert and amateur models in
speculative decoding. Comprehensive evaluations on four benchmarks show that
SCD can achieve similar acceleration factors as speculative decoding while
further improving the generation quality as the contrastive decoding. The
analysis of token probabilities further demonstrates the compatibility between
speculative and contrastive decoding. Overall, SCD provides an effective
approach to enhance the decoding quality of LLMs while saving computational
resources.Comment: Working in Progres
Width-tuned magnetic order oscillation on zigzag edges of honeycomb nanoribbons
Quantum confinement and interference often generate exotic properties in
nanostructures. One recent highlight is the experimental indication of a
magnetic phase transition in zigzag-edged graphene nanoribbons at the critical
ribbon width of about 7 nm [G. Z. Magda et al., Nature \textbf{514}, 608
(2014)]. Here we show theoretically that with further increase in the ribbon
width, the magnetic correlation of the two edges can exhibit an intriguing
oscillatory behavior between antiferromagnetic and ferromagnetic, driven by
acquiring the positive coherence between the two edges to lower the free
energy. The oscillation effect is readily tunable in applied magnetic fields.
These novel properties suggest new experimental manifestation of the edge
magnetic orders in graphene nanoribbons, and enhance the hopes of graphene-like
spintronic nanodevices functioning at room temperature.Comment: 22 pages, 9 figure
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
The complementary potential of Large Language Models (LLM) assumes
off-the-shelf LLMs have heterogeneous expertise in a wide range of domains and
tasks so that an ensemble of LLMs can achieve consistently better performance.
Existing ensemble methods for LLMs mainly focus on reward model ranking of
outputs, leading to significant computation overhead. To combat this issue, we
revisit the complementary potential of LLMs and further elaborate it by mining
latent expertise with off-the-shelf reward models. We propose Zooter, a
reward-guided routing method distilling rewards on training queries to train a
routing function, which can precisely distribute each query to the LLM with
expertise about it. We also integrate a tag-based label enhancement to mitigate
noise from uncertainty when using rewards as silver supervision. Zooter shows
computation efficiency in inference as it introduces only a minor computation
overhead of a routing function compared with reward model ranking methods. We
evaluate Zooter on a comprehensive benchmark collection with 26 subsets on
different domains and tasks. Zooter outperforms the best single model on
average and ranks first on 44% of tasks, even surpassing multiple reward model
ranking methods
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Foundation language models obtain the instruction-following ability through
supervised fine-tuning (SFT). Diversity and complexity are considered critical
factors of a successful SFT dataset, while their definitions remain obscure and
lack quantitative analyses. In this work, we propose InsTag, an open-set
fine-grained tagger, to tag samples within SFT datasets based on semantics and
intentions and define instruction diversity and complexity regarding tags. We
obtain 6.6K tags to describe comprehensive user queries. Then we analyze
popular open-sourced SFT datasets and find that the model ability grows with
more diverse and complex data. Based on this observation, we propose a data
selector based on InsTag to select 6K diverse and complex samples from
open-source datasets and fine-tune models on InsTag-selected data. The
resulting models, TagLM, outperform open-source models based on considerably
larger SFT data evaluated by MT-Bench, echoing the importance of query
diversity and complexity. We open-source InsTag in
https://github.com/OFA-Sys/InsTag
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