GLiNER

In the experiment, the GLiNER model acting alone exhibited a single behavior pattern, performing almost only shooting actions.

In a hierarchical agent architecture, the LLM does not need to participate in every frame's decision, greatly reducing inference cost; GLiNER as the executor can maintain low-latency responses.

GLiNER alone lacks any concept of health management and only keeps shooting until it dies.

The LLM coach can dynamically adjust strategy based on state changes, for example reminding the character to grab a medkit when the player's health drops.

In the experiment, GLiNER running alone occasionally exhibited meaningless actions such as spinning in place.

In this experiment, GLiNER was used as a vision-to-action mapper, receiving game frames, recognizing entities such as enemies, weapons and items, and outputting the corresponding immediate actions.

GLiNER supports describing arbitrary entity types in natural language, enabling zero-shot recognition without retraining for new categories.

In the combined system, the LLM does not directly control the character but acts as a tactical guide, issuing natural-language instructions to GLiNER based on the game state.

GLiNER is a class of lightweight models for general-purpose named entity recognition that uses a bidirectional Transformer encoder to process entity types and text simultaneously, without needing to train separately for each entity type.

The creator initially planned to use the GLiNER decision model, but found that the locally runnable GLiNER is not open source, so switched to GLiNER 2.5.

Agentic GraphRAG's entity extraction is driven by a user-defined schema, with local extraction using GLiNER and structured LLM extraction handled by BAML on selected chunks.

GLiNER is a zero-shot named entity recognition model based on a bidirectional encoder that accepts an arbitrary list of entity types at inference time without fine-tuning a model for each domain.

Entity extraction in Agentic GraphRAG can use a locally running GLiNER model, or BAML can be used for structured LLM extraction on selected chunks.

GLiNER is a family of models whose name derives from named entity recognition, with the earliest version tracing back to 2023.

On the Deathmatch map, GLiNER with an LLM coach more than doubled its score; local GLiNER achieves approximately 8.8 decisions per second, whereas the hosted Jev is limited to only 2.5 decisions per second due to network latency.

Bidirectional encoder models can simultaneously see left and right context at every position, making them suitable for understanding tasks such as classification and entity recognition; both GLiNER and Jev are based on the bidirectional encoder approach.

GLiNER does not provide calibrated probabilities, does not give a full probability distribution, and requires switching between different models for different use cases (GLiNER 2.5, GlyGuard, etc.).

The zero-shot span-based GLiNER model almost completely fails on entity types that depend on institutional function, achieving a micro F1 of only 0.243.

GLiNER is a class of open NER models that supports zero-shot recognition of entity types through natural language descriptions, requiring no retraining for each new type.